1
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Tahvili F, Destexhe A. A mean-field model of gamma-frequency oscillations in networks of excitatory and inhibitory neurons. J Comput Neurosci 2024; 52:165-181. [PMID: 38512693 DOI: 10.1007/s10827-024-00867-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024]
Abstract
Gamma oscillations are widely seen in the cerebral cortex in different states of the wake-sleep cycle and are thought to play a role in sensory processing and cognition. Here, we study the emergence of gamma oscillations at two levels, in networks of spiking neurons, and a mean-field model. At the network level, we consider two different mechanisms to generate gamma oscillations and show that they are best seen if one takes into account the synaptic delay between neurons. At the mean-field level, we show that, by introducing delays, the mean-field can also produce gamma oscillations. The mean-field matches the mean activity of excitatory and inhibitory populations of the spiking network, as well as their oscillation frequencies, for both mechanisms. This mean-field model of gamma oscillations should be a useful tool to investigate large-scale interactions through gamma oscillations in the brain.
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Affiliation(s)
- Farzin Tahvili
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, CNRS, 91400, Saclay, France
- Stem-cell & Brain Research Institute (SBRI), 69500, Bron Cedex, France
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, CNRS, 91400, Saclay, France.
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2
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Luppi AI, Uhrig L, Tasserie J, Signorelli CM, Stamatakis EA, Destexhe A, Jarraya B, Cofre R. Local orchestration of distributed functional patterns supporting loss and restoration of consciousness in the primate brain. Nat Commun 2024; 15:2171. [PMID: 38462641 PMCID: PMC10925605 DOI: 10.1038/s41467-024-46382-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 02/26/2024] [Indexed: 03/12/2024] Open
Abstract
A central challenge of neuroscience is to elucidate how brain function supports consciousness. Here, we combine the specificity of focal deep brain stimulation with fMRI coverage of the entire cortex, in awake and anaesthetised non-human primates. During propofol, sevoflurane, or ketamine anaesthesia, and subsequent restoration of responsiveness by electrical stimulation of the central thalamus, we investigate how loss of consciousness impacts distributed patterns of structure-function organisation across scales. We report that distributed brain activity under anaesthesia is increasingly constrained by brain structure across scales, coinciding with anaesthetic-induced collapse of multiple dimensions of hierarchical cortical organisation. These distributed signatures are observed across different anaesthetics, and they are reversed by electrical stimulation of the central thalamus, coinciding with recovery of behavioural markers of arousal. No such effects were observed upon stimulating the ventral lateral thalamus, demonstrating specificity. Overall, we identify consistent distributed signatures of consciousness that are orchestrated by specific thalamic nuclei.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Lynn Uhrig
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Department of Anesthesiology and Critical Care, Necker Hospital, AP-HP, Université de Paris Cité, Paris, France
| | - Jordy Tasserie
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Camilo M Signorelli
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, 1070, Brussels, Belgium
- Department of Computer Science, University of Oxford, Oxford, 7 Parks Rd, Oxford, OX1 3QG, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
| | - Bechir Jarraya
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Department of Neurology, Hopital Foch, 92150, Suresnes, France
| | - Rodrigo Cofre
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France.
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3
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Aquilué-Llorens D, Goldman JS, Destexhe A. High-Density Exploration of Activity States in a Multi-Area Brain Model. Neuroinformatics 2024; 22:75-87. [PMID: 37981636 PMCID: PMC10917847 DOI: 10.1007/s12021-023-09647-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2023] [Indexed: 11/21/2023]
Abstract
To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e.g. neuromodulatory regulation of spike-frequency adaptation during sleep-wake cycles or anesthetics). Using the Virtual Brain (TVB) environment to connect mean-field AdEx models, we have previously simulated the general properties of brain states, playing on spike-frequency adaptation, but have not yet performed detailed analyses of other parameters possibly also regulating transitions in brain-scale dynamics between different brain states. We performed a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing. We report a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the TVB-AdEx model reveals large-scale properties identified experimentally in sleep and anesthesia.
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Affiliation(s)
- David Aquilué-Llorens
- Paris-Saclay University, CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), 91400, Saclay, France.
- Starlab Barcelona SL, Neuroscience BU, Av Tibidabo 47 bis, Barcelona, Spain.
| | - Jennifer S Goldman
- Paris-Saclay University, CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), 91400, Saclay, France
| | - Alain Destexhe
- Paris-Saclay University, CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), 91400, Saclay, France.
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4
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Susin E, Destexhe A. A Network Model of the Modulation of γ Oscillations by NMDA Receptors in Cerebral Cortex. eNeuro 2023; 10:ENEURO.0157-23.2023. [PMID: 37940562 PMCID: PMC10668239 DOI: 10.1523/eneuro.0157-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 11/10/2023] Open
Abstract
Psychotic drugs such as ketamine induce symptoms close to schizophrenia and stimulate the production of γ oscillations, as also seen in patients, but the underlying mechanisms are still unclear. Here, we have used computational models of cortical networks generating γ oscillations, and have integrated the action of drugs such as ketamine to partially block NMDA receptors (NMDARs). The model can reproduce the paradoxical increase of γ oscillations by NMDA receptor antagonists, assuming that antagonists affect NMDA receptors with higher affinity on inhibitory interneurons. We next used the model to compare the responsiveness of the network to external stimuli, and found that when NMDA channels are blocked, an increase of γ power is observed altogether with an increase of network responsiveness. However, this responsiveness increase applies not only to γ states, but also to asynchronous states with no apparent γ. We conclude that NMDA antagonists induce an increased excitability state, which may or may not produce γ oscillations, but the response to external inputs is exacerbated, which may explain phenomena such as altered perception or hallucinations.
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Affiliation(s)
- Eduarda Susin
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Saclay, France 91400
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Saclay, France 91400
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5
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Lorenzi RM, Geminiani A, Zerlaut Y, De Grazia M, Destexhe A, Gandini Wheeler-Kingshott CAM, Palesi F, Casellato C, D'Angelo E. A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics. PLoS Comput Biol 2023; 19:e1011434. [PMID: 37656758 PMCID: PMC10501640 DOI: 10.1371/journal.pcbi.1011434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 09/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.
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Affiliation(s)
| | - Alice Geminiani
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Yann Zerlaut
- Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | | | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Claudia Casellato
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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6
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Tesler F, Linne ML, Destexhe A. Modeling the relationship between neuronal activity and the BOLD signal: contributions from astrocyte calcium dynamics. Sci Rep 2023; 13:6451. [PMID: 37081004 PMCID: PMC10119111 DOI: 10.1038/s41598-023-32618-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/30/2023] [Indexed: 04/22/2023] Open
Abstract
Functional magnetic resonance imaging relies on the coupling between neuronal and vascular activity, but the mechanisms behind this coupling are still under discussion. Recent experimental evidence suggests that calcium signaling may play a significant role in neurovascular coupling. However, it is still controversial where this calcium signal is located (in neurons or elsewhere), how it operates and how relevant is its role. In this paper we introduce a biologically plausible model of the neurovascular coupling and we show that calcium signaling in astrocytes can explain main aspects of the dynamics of the coupling. We find that calcium signaling can explain so-far unrelated features such as the linear and non-linear regimes, the negative vascular response (undershoot) and the emergence of a (calcium-driven) Hemodynamic Response Function. These features are reproduced here for the first time by a single model of the detailed neuronal-astrocyte-vascular pathway. Furthermore, we analyze how information is coded and transmitted from the neuronal to the vascular system and we predict that frequency modulation of astrocytic calcium dynamics plays a key role in this process. Finally, our work provides a framework to link neuronal activity to the BOLD signal, and vice-versa, where neuronal activity can be inferred from the BOLD signal. This opens new ways to link known alterations of astrocytic calcium signaling in neurodegenerative diseases (e.g. Alzheimer's and Parkinson's diseases) with detectable changes in the neurovascular coupling.
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Affiliation(s)
- Federico Tesler
- CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), Paris-Saclay University, 91400, Saclay, France.
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere University, 33720, Tampere, Finland
| | - Alain Destexhe
- CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), Paris-Saclay University, 91400, Saclay, France
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7
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Goldman JS, Kusch L, Aquilue D, Yalçınkaya BH, Depannemaecker D, Ancourt K, Nghiem TAE, Jirsa V, Destexhe A. A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics. Front Comput Neurosci 2023; 16:1058957. [PMID: 36714530 PMCID: PMC9880280 DOI: 10.3389/fncom.2022.1058957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/21/2022] [Indexed: 01/15/2023] Open
Abstract
Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirically observed spontaneous and stimulus-evoked dynamics in space, time, phase, and frequency domains. Large-scale properties of cortical dynamics are shown to emerge from both microscopic-scale adaptation that control transitions between wake-like to sleep-like activity, and the organization of the human structural connectome; together, they shape the spatial extent of synchrony and phase coherence across brain regions consistent with the propagation of sleep-like spontaneous traveling waves at intermediate scales. Remarkably, the model also reproduces brain-wide, enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.
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Affiliation(s)
- Jennifer S. Goldman
- CNRS, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Saclay, France,*Correspondence: Jennifer S. Goldman ✉
| | - Lionel Kusch
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
| | - David Aquilue
- CNRS, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Saclay, France
| | - Bahar Hazal Yalçınkaya
- CNRS, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Saclay, France,Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
| | | | - Kevin Ancourt
- CNRS, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Saclay, France
| | - Trang-Anh E. Nghiem
- CNRS, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Saclay, France,Laboratoire de Physique, Ecole Normale Supérieure, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
| | - Alain Destexhe
- CNRS, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Saclay, France,Alain Destexhe ✉
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8
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Casas-Torremocha D, Cortada M, Camassa A, Tort-Colet N, Destexhe A, Sánchez-Vives MV. Perturbational cortical complexity evoked by thalamic versus motor cortex stimulation. Brain Stimul 2023. [DOI: 10.1016/j.brs.2023.01.798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
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9
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Destexhe A. Noise Enhancement of Neural Information Processing. Entropy (Basel) 2022; 24:1837. [PMID: 36554242 PMCID: PMC9778153 DOI: 10.3390/e24121837] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Cortical neurons in vivo function in highly fluctuating and seemingly noisy conditions, and the understanding of how information is processed in such complex states is still incomplete. In this perspective article, we first overview that an intense "synaptic noise" was measured first in single neurons, and computational models were built based on such measurements. Recent progress in recording techniques has enabled the measurement of highly complex activity in large numbers of neurons in animals and human subjects, and models were also built to account for these complex dynamics. Here, we attempt to link these two cellular and population aspects, where the complexity of network dynamics in awake cortex seems to link to the synaptic noise seen in single cells. We show that noise in single cells, in networks, or structural noise, all participate to enhance responsiveness and boost the propagation of information. We propose that such noisy states are fundamental to providing favorable conditions for information processing at large-scale levels in the brain, and may be involved in sensory perception.
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Affiliation(s)
- Alain Destexhe
- CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), Paris-Saclay University, 91400 Saclay, France
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10
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Tesler F, Tort-Colet N, Depannemaecker D, Carlu M, Destexhe A. Mean-field based framework for forward modeling of LFP and MEG signals. Front Comput Neurosci 2022; 16:968278. [PMID: 36313811 PMCID: PMC9606720 DOI: 10.3389/fncom.2022.968278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/17/2022] [Indexed: 11/20/2022] Open
Abstract
The use of mean-field models to describe the activity of large neuronal populations has become a very powerful tool for large-scale or whole brain simulations. However, the calculation of brain signals from mean-field models, such as the electric and magnetic fields, is still under development. Thus, the emergence of new methods for an accurate and efficient calculation of such brain signals is currently of great relevance. In this paper we propose a novel method to calculate the local field potentials (LFP) and magnetic fields from mean-field models. The calculation of LFP is done via a kernel method based on unitary LFP's (the LFP generated by a single axon) that was recently introduced for spiking-networks simulations and that we adapt here for mean-field models. The calculation of the magnetic field is based on current-dipole and volume-conductor models, where the secondary currents (due to the conducting extracellular medium) are estimated using the LFP calculated via the kernel method and the effects of medium-inhomogeneities are incorporated. We provide an example of the application of our method for the calculation of LFP and MEG under slow-waves of neuronal activity generated by a mean-field model of a network of Adaptive-Exponential Integrate-and-Fire (AdEx) neurons. We validate our method via comparison with results obtained from the corresponding spiking neuronal networks. Finally we provide an example of our method for whole brain simulations performed with The Virtual Brain (TVB), a recently developed tool for large scale simulations of the brain. Our method provides an efficient way of calculating electric and magnetic fields from mean-field models. This method exhibits a great potential for its application in large-scale or whole-brain simulations, where calculations via detailed biological models are not feasible.
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11
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Filipchuk A, Schwenkgrub J, Destexhe A, Bathellier B. Awake perception is associated with dedicated neuronal assemblies in the cerebral cortex. Nat Neurosci 2022; 25:1327-1338. [PMID: 36171431 PMCID: PMC9534770 DOI: 10.1038/s41593-022-01168-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 08/18/2022] [Indexed: 12/05/2022]
Abstract
Neural activity in the sensory cortex combines stimulus responses and ongoing activity, but it remains unclear whether these reflect the same underlying dynamics or separate processes. In the present study, we show in mice that, during wakefulness, the neuronal assemblies evoked by sounds in the auditory cortex and thalamus are specific to the stimulus and distinct from the assemblies observed in ongoing activity. By contrast, under three different anesthetics, evoked assemblies are indistinguishable from ongoing assemblies in the cortex. However, they remain distinct in the thalamus. A strong remapping of sensory responses accompanies this dynamic state change produced by anesthesia. Together, these results show that the awake cortex engages dedicated neuronal assemblies in response to sensory inputs, which we suggest is a network correlate of sensory perception. Filipchuk et al. show that when awake mice perceive sounds, the auditory cortex produces sound-specific neuronal assemblies distinct from its ongoing activity, whereas under anesthesia sound-evoked assemblies are indistinguishable from ongoing activity.
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Affiliation(s)
- Anton Filipchuk
- Paris-Saclay University, CNRS, Paris-Saclay Institute of Neuroscience, Saclay, France.,Healthy Mind, Institut du Cerveau - ICM, Paris, France
| | - Joanna Schwenkgrub
- Institut Pasteur, Université de Paris, INSERM, Institut de l'Audition, Paris, France
| | - Alain Destexhe
- Paris-Saclay University, CNRS, Paris-Saclay Institute of Neuroscience, Saclay, France.
| | - Brice Bathellier
- Paris-Saclay University, CNRS, Paris-Saclay Institute of Neuroscience, Saclay, France. .,Institut Pasteur, Université de Paris, INSERM, Institut de l'Audition, Paris, France.
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12
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Destexhe A, Foubert L. A method to convert neural signals into sound sequences. J Acoust Soc Am 2022; 151:3685. [PMID: 35778195 DOI: 10.1121/10.0011549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 05/14/2022] [Indexed: 06/15/2023]
Abstract
We present a method to convert neural signals into sound sequences, with the constraint that the sound sequences precisely reflect the sequences of events in the neural signal. The method consists in quantifying the wave motifs in the signal and using these parameters to generate sound envelopes. We illustrate the procedure for sleep delta waves in the human electro-encephalogram (EEG), which are converted into sound sequences that encode the time structure of the original EEG waves. This procedure can be applied to synthesize personalized sound sequences specific to the EEG of a given subject.
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Affiliation(s)
- Alain Destexhe
- Paris-Saclay University, CNRS, Institute of Neuroscience, 91400 Saclay, France
| | - Luc Foubert
- Paris-Saclay University, CNRS, Institute of Neuroscience, 91400 Saclay, France
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13
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Bedard C, Piette C, Venance L, Destexhe A. Extracellular and intracellular components of the impedance of neural tissue. Biophys J 2022; 121:869-885. [PMID: 35182541 PMCID: PMC8943819 DOI: 10.1016/j.bpj.2022.02.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 11/29/2021] [Accepted: 02/11/2022] [Indexed: 11/30/2022] Open
Abstract
Electric phenomena in brain tissue can be measured using extracellular potentials, such as the local field potential, or the electro-encephalogram. The interpretation of these signals depends on the electric structure and properties of extracellular media, but the measurements of these electric properties are still debated. Some measurements point to a model in which the extracellular medium is purely resistive, and thus parameters such as electric conductivity and permittivity should be independent of frequency. Other measurements point to a pronounced frequency dependence of these parameters, with scaling laws that are consistent with capacitive or diffusive effects. However, these experiments correspond to different preparations, and it is unclear how to correctly compare them. Here, we provide for the first time, impedance measurements (in the 1-10 kHz frequency range) using the same setup in various preparations, from primary cell cultures to acute brain slices, and a comparison with similar measurements performed in artificial cerebrospinal fluid with no biological material. The measurements show that when the current flows across a cell membrane, the frequency dependence of the macroscopic impedance between intracellular and extracellular electrodes is significant, and cannot be captured by a model with resistive media. Fitting a mean-field model to the data shows that this frequency dependence could be explained by the ionic diffusion mainly associated with Debye layers surrounding the membranes. We conclude that neuronal membranes and their ionic environment induce strong deviations to resistivity that should be taken into account to correctly interpret extracellular potentials generated by neurons.
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Affiliation(s)
- Claude Bedard
- Paris-Saclay University, CNRS, Institute of Neuroscience (NeuroPSI), Gif sur Yvette, France
| | - Charlotte Piette
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL University, Paris, France
| | - Laurent Venance
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL University, Paris, France
| | - Alain Destexhe
- Paris-Saclay University, CNRS, Institute of Neuroscience (NeuroPSI), Gif sur Yvette, France.
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14
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Susin E, Destexhe A. Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states. PLoS Comput Biol 2021; 17:e1009416. [PMID: 34529655 PMCID: PMC8478196 DOI: 10.1371/journal.pcbi.1009416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/28/2021] [Accepted: 09/02/2021] [Indexed: 12/29/2022] Open
Abstract
Gamma oscillations are widely seen in the awake and sleeping cerebral cortex, but the exact role of these oscillations is still debated. Here, we used biophysical models to examine how Gamma oscillations may participate to the processing of afferent stimuli. We constructed conductance-based network models of Gamma oscillations, based on different cell types found in cerebral cortex. The models were adjusted to extracellular unit recordings in humans, where Gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which Gamma is generated by interneuron networks (ING) and third, a mechanism which relies on Gamma oscillations generated by pacemaker chattering neurons (CHING). We find that all three mechanisms generate features consistent with human recordings, but that the ING mechanism is most consistent with the firing rate change inside Gamma bursts seen in the human data. We next evaluated the responsiveness and resonant properties of these networks, contrasting Gamma oscillations with the asynchronous mode. We find that for both slowly-varying stimuli and precisely-timed stimuli, the responsiveness is generally lower during Gamma compared to asynchronous states, while resonant properties are similar around the Gamma band. We could not find conditions where Gamma oscillations were more responsive. We therefore predict that asynchronous states provide the highest responsiveness to external stimuli, while Gamma oscillations tend to overall diminish responsiveness. In the awake and attentive brain, the activity of neurons is typically asynchronous and irregular. It also occasionally displays oscillations in the Gamma frequency range (30–90 Hz), which are believed to be involved in information processing. Here, we use computational models to investigate how brain circuits generate oscillations in a manner consistent with microelectrode recordings in humans. We then study how these networks respond to external input, comparing asynchronous and oscillatory states. This is tested according to several paradigms, an integrative mode, where slowly varying inputs are progressively integrated, a coincidence detection mode, where brief inputs are processed according to the phase of the oscillations, and a resonance mode where the network is probed with oscillatory inputs. Surprisingly, we find that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness to external inputs. We discuss possible implications of this responsiveness decrease on information processing and propose new directions for further exploration.
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Affiliation(s)
- Eduarda Susin
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
- * E-mail:
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
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15
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Abstract
Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.
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Affiliation(s)
- Matteo Di Volo
- Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, 95302, Cergy-Pontoise cedex, France.
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif sur Yvette, France
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16
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Depannemaecker D, Destexhe A, Jirsa V, Bernard C. Modeling seizures: From single neurons to networks. Seizure 2021; 90:4-8. [PMID: 34219016 DOI: 10.1016/j.seizure.2021.06.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/11/2021] [Accepted: 06/11/2021] [Indexed: 11/26/2022] Open
Abstract
Dynamical system tools offer a complementary approach to detailed biophysical seizure modeling, with a high potential for clinical applications. This review describes the theoretical framework that provides a basis for theorizing certain properties of seizures and for their classification according to their dynamical properties at onset and offset. We describe various modeling approaches spanning different scales, from single neurons to large-scale networks. This narrative review provides an accessible overview of this field, including non-exhaustive examples of key recent works.
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Affiliation(s)
- Damien Depannemaecker
- Paris-Saclay University, French National Centre for Scientific Research (CNRS), Institute of Neuroscience (NeuroPSI), 91198 Gif sur Yvette, France.
| | - Alain Destexhe
- Paris-Saclay University, French National Centre for Scientific Research (CNRS), Institute of Neuroscience (NeuroPSI), 91198 Gif sur Yvette, France.
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Institut des Neurosciences des Systèmes, Marseille, France.
| | - Christophe Bernard
- Aix Marseille Univ, INSERM, INS, Institut des Neurosciences des Systèmes, Marseille, France.
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17
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di Volo M, Chemla S, Destexhe A. Cortical propagating waves: amplifying and suppressive? J Comput Neurosci 2021; 49:371-373. [PMID: 34003423 DOI: 10.1007/s10827-021-00792-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/12/2021] [Accepted: 05/14/2021] [Indexed: 11/28/2022]
Affiliation(s)
- Matteo di Volo
- Paris-Saclay University, CNRS, Gif-sur-Yvette, France.,Paris-Cergy University, Cergy, France
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18
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Abstract
This commentary puts the In Silico movie in perspective of the Human Brain Project (HBP) and clarifies major differences between this project and the Blue Brain Project, emphasizing that the two projects are very different in scope.
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Affiliation(s)
- Alain Destexhe
- Paris-Saclay University, Centre National de la Recherche Scientifique, 91198 Gif sur Yvette, France
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19
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Destexhe A, Victor JD. Editorial: new article type "perspective". J Comput Neurosci 2021; 49:69. [PMID: 33646475 DOI: 10.1007/s10827-021-00781-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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20
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Rebollo B, Telenczuk B, Navarro-Guzman A, Destexhe A, Sanchez-Vives MV. Modulation of intercolumnar synchronization by endogenous electric fields in cerebral cortex. Sci Adv 2021; 7:7/10/eabc7772. [PMID: 33658192 PMCID: PMC7929504 DOI: 10.1126/sciadv.abc7772] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 01/21/2021] [Indexed: 06/01/2023]
Abstract
Neurons synaptically interacting in a conductive medium generate extracellular endogenous electric fields (EFs) that reciprocally affect membrane potential. Exogenous EFs modulate neuronal activity, and their clinical applications are being profusely explored. However, whether endogenous EFs contribute to network synchronization remains unclear. We analyzed spontaneously generated slow-wave activity in the cerebral cortex network in vitro, which allowed us to distinguish synaptic from nonsynaptic mechanisms of activity propagation and synchronization. Slow oscillations generated EFs that propagated independently of synaptic transmission. We demonstrate that cortical oscillations modulate spontaneous rhythmic activity of neighboring synaptically disconnected cortical columns if layers are aligned. We provide experimental evidence that these EF-mediated effects are compatible with electric dipoles. With a model of interacting dipoles, we reproduce the experimental measurements and predict that endogenous EF-mediated synchronizing effects should be relevant in the brain. Thus, experiments and models suggest that electric-dipole interactions contribute to synchronization of neighboring cortical columns.
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Affiliation(s)
- Beatriz Rebollo
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Bartosz Telenczuk
- Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS), Institut des Neurosciences, Gif sur Yvette, France
| | - Alvaro Navarro-Guzman
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Alain Destexhe
- Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS), Institut des Neurosciences, Gif sur Yvette, France
| | - Maria V Sanchez-Vives
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
- ICREA, Barcelona, Spain
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21
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Abstract
The intrinsic electrophysiological properties of single neurons can be described by a broad spectrum of models, from realistic Hodgkin-Huxley-type models with numerous detailed mechanisms to the phenomenological models. The adaptive exponential integrate-and-fire (AdEx) model has emerged as a convenient middle-ground model. With a low computational cost but keeping biophysical interpretation of the parameters, it has been extensively used for simulations of large neural networks. However, because of its current-based adaptation, it can generate unrealistic behaviors. We show the limitations of the AdEx model, and to avoid them, we introduce the conductance-based adaptive exponential integrate-and-fire model (CAdEx). We give an analysis of the dynamics of the CAdEx model and show the variety of firing patterns it can produce. We propose the CAdEx model as a richer alternative to perform network simulations with simplified models reproducing neuronal intrinsic properties.
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Affiliation(s)
- Tomasz Górski
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France
| | - Damien Depannemaecker
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France
| | - Alain Destexhe
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France
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22
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Telenczuk B, Telenczuk M, Destexhe A. A kernel-based method to calculate local field potentials from networks of spiking neurons. J Neurosci Methods 2020; 344:108871. [PMID: 32687850 DOI: 10.1016/j.jneumeth.2020.108871] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND The local field potential (LFP) is usually calculated from current sources arising from transmembrane currents, in particular in asymmetric cellular morphologies such as pyramidal neurons. NEW METHOD Here, we adopt a different point of view and relate the spiking of neurons to the LFP through efferent synaptic connections and provide a method to calculate LFPs. RESULTS We show that the so-called unitary LFPs (uLFP) provide the key to such a calculation. We show experimental measurements and simulations of uLFPs in neocortex and hippocampus, for both excitatory and inhibitory neurons. We fit a "kernel" function to measurements of uLFPs, and we estimate its spatial and temporal spread by using simulations of morphologically detailed reconstructions of hippocampal pyramidal neurons. Assuming that LFPs are the sum of uLFPs generated by every neuron in the network, the LFP generated by excitatory and inhibitory neurons can be calculated by convolving the trains of action potentials with the kernels estimated from uLFPs. This provides a method to calculate the LFP from networks of spiking neurons, even for point neurons for which the LFP is not easily defined. We show examples of LFPs calculated from networks of point neurons and compare to the LFP calculated from synaptic currents. CONCLUSIONS The kernel-based method provides a practical way to calculate LFPs from networks of point neurons.
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Affiliation(s)
- Bartosz Telenczuk
- Paris-Saclay University, Institute of Neuroscience (NeuroPSI), CNRS, 1 Avenue de la Terrasse, 91198 Gif sur Yvette, France
| | - Maria Telenczuk
- Paris-Saclay University, Institute of Neuroscience (NeuroPSI), CNRS, 1 Avenue de la Terrasse, 91198 Gif sur Yvette, France
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience (NeuroPSI), CNRS, 1 Avenue de la Terrasse, 91198 Gif sur Yvette, France.
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23
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Teleńczuk M, Teleńczuk B, Destexhe A. Modelling unitary fields and the single-neuron contribution to local field potentials in the hippocampus. J Physiol 2020; 598:3957-3972. [PMID: 32598027 PMCID: PMC7540286 DOI: 10.1113/jp279452] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 06/17/2020] [Indexed: 11/08/2022] Open
Abstract
Key points We simulate the unitary local field potential (uLFP) generated in the hippocampus CA3, using morphologically detailed models. The model suggests that cancelling effects between apical and basal dendritic synapses explain the low amplitude of excitatory uLFPs. Inhibitory synapses around the soma do not cancel and could explain the high‐amplitude inhibitory uLFPs. These results suggest that somatic inhibition constitutes a strong component of LFPs, which may explain a number of experimental observations.
Abstract Synaptic currents represent a major contribution to the local field potential (LFP) in brain tissue, but the respective contribution of excitatory and inhibitory synapses is not known. Here, we provide estimates of this contribution by using computational models of hippocampal pyramidal neurons, constrained by in vitro recordings. We focus on the unitary LFP (uLFP) generated by single neurons in the CA3 region of the hippocampus. We first reproduce experimental results for hippocampal basket cells, and in particular how inhibitory uLFP are distributed within hippocampal layers. Next, we calculate the uLFP generated by pyramidal neurons, using morphologically reconstructed CA3 pyramidal cells. The model shows that the excitatory uLFP is of small amplitude, smaller than inhibitory uLFPs. Indeed, when the two are simulated together, inhibitory uLFPs mask excitatory uLFPs, which might create the illusion that the inhibitory field is generated by pyramidal cells. These results provide an explanation for the observation that excitatory and inhibitory uLFPs are of the same polarity, in vivo and in vitro. These results suggest that somatic inhibitory currents are large contributors to the LFP, which is important information for interpreting this signal. Finally, the results of our model might form the basis of a simple method to compute the LFP, which could be applied to point neurons for each cell type, thus providing a simple biologically grounded method for calculating LFPs from neural networks. In conclusion, computational models constrained by in vitro recordings suggest that: (1) Excitatory uLFPs are of smaller amplitude than inhibitory uLFPs. (2) Inhibitory uLFPs form the major contribution to LFPs. (3) uLFPs can be used as a simple model to generate LFPs from spiking networks. We simulate the unitary local field potential (uLFP) generated in the hippocampus CA3, using morphologically detailed models. The model suggests that cancelling effects between apical and basal dendritic synapses explain the low amplitude of excitatory uLFPs. Inhibitory synapses around the soma do not cancel and could explain the high‐amplitude inhibitory uLFPs. These results suggest that somatic inhibition constitutes a strong component of LFPs, which may explain a number of experimental observations.
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Affiliation(s)
- Maria Teleńczuk
- Paris-Saclay University, Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique, Gif-sur-Yvette, 91198, France
| | - Bartosz Teleńczuk
- Paris-Saclay University, Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique, Gif-sur-Yvette, 91198, France
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique, Gif-sur-Yvette, 91198, France
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24
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Allegra Mascaro AL, Falotico E, Petkoski S, Pasquini M, Vannucci L, Tort-Colet N, Conti E, Resta F, Spalletti C, Ramalingasetty ST, von Arnim A, Formento E, Angelidis E, Blixhavn CH, Leergaard TB, Caleo M, Destexhe A, Ijspeert A, Micera S, Laschi C, Jirsa V, Gewaltig MO, Pavone FS. Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience. Front Syst Neurosci 2020; 14:31. [PMID: 32733210 PMCID: PMC7359878 DOI: 10.3389/fnsys.2020.00031] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 05/08/2020] [Indexed: 01/22/2023] Open
Abstract
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
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Affiliation(s)
- Anna Letizia Allegra Mascaro
- Neuroscience Institute, National Research Council, Pisa, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Egidio Falotico
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Spase Petkoski
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Maria Pasquini
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Núria Tort-Colet
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Emilia Conti
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Francesco Resta
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | | | | | | | - Emanuele Formento
- Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Emmanouil Angelidis
- Fortiss GmbH, Munich, Germany.,Chair of Robotics, Artificial Intelligence and Embedded Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | | | | | - Matteo Caleo
- Neuroscience Institute, National Research Council, Pisa, Italy.,Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Auke Ijspeert
- Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Silvestro Micera
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Cecilia Laschi
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Viktor Jirsa
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Marc-Oliver Gewaltig
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Francesco S Pavone
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
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25
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26
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Capone C, di Volo M, Romagnoni A, Mattia M, Destexhe A. State-dependent mean-field formalism to model different activity states in conductance-based networks of spiking neurons. Phys Rev E 2020; 100:062413. [PMID: 31962518 DOI: 10.1103/physreve.100.062413] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Indexed: 11/07/2022]
Abstract
More interest has been shown in recent years to large-scale spiking simulations of cerebral neuronal networks, coming both from the presence of high-performance computers and increasing details in experimental observations. In this context it is important to understand how population dynamics are generated by the designed parameters of the networks, which is the question addressed by mean-field theories. Despite analytic solutions for the mean-field dynamics already being proposed for current-based neurons (CUBA), a complete analytic description has not been achieved yet for more realistic neural properties, such as conductance-based (COBA) network of adaptive exponential neurons (AdEx). Here, we propose a principled approach to map a COBA on a CUBA. Such an approach provides a state-dependent approximation capable of reliably predicting the firing-rate properties of an AdEx neuron with noninstantaneous COBA integration. We also applied our theory to population dynamics, predicting the dynamical properties of the network in very different regimes, such as asynchronous irregular and synchronous irregular (slow oscillations). This result shows that a state-dependent approximation can be successfully introduced to take into account the subtle effects of COBA integration and to deal with a theory capable of correctly predicting the activity in regimes of alternating states like slow oscillations.
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Affiliation(s)
- Cristiano Capone
- INFN, Sezione di Roma, 00185 Rome, Italy and Department of Integrative and Computational Neuroscience (ICN), Paris- Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91198 Gif-sur-Yvette, France
| | - Matteo di Volo
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Théorique et Modelisation, Université de Cergy-Pontoise, 95302 Cergy-Pontoise cedex, France
| | - Alberto Romagnoni
- Data Team, Département d'informatique de l'ENS, École normale supérieure France, CNRS, PSL Research University, 75005 Paris France and Centre de recherche sur linflammation UMR 1149, Inserm-Universit Paris Diderot, Paris, France
| | - Maurizio Mattia
- National Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanitá, 00161 Rome, Italy
| | - Alain Destexhe
- Department of Integrative and Computational Neuroscience (ICN), Paris- Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91198 Gif-sur-Yvette, France
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27
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Carlu M, Chehab O, Dalla Porta L, Depannemaecker D, Héricé C, Jedynak M, Köksal Ersöz E, Muratore P, Souihel S, Capone C, Zerlaut Y, Destexhe A, di Volo M. A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models. J Neurophysiol 2020; 123:1042-1051. [PMID: 31851573 PMCID: PMC7099478 DOI: 10.1152/jn.00399.2019] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 12/05/2019] [Accepted: 12/09/2019] [Indexed: 11/22/2022] Open
Abstract
We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the population response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology.NEW & NOTEWORTHY Population models are a powerful mathematical tool to study the dynamics of neuronal networks and to simulate the brain at macroscopic scales. We present a mean-field model capable of quantitatively predicting the temporal dynamics of a network of complex spiking neuronal models, from Integrate-and-Fire to Hodgkin-Huxley, thus linking population models to neurons electrophysiology. This opens a perspective on generating biologically realistic mean-field models from electrophysiological recordings.
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Affiliation(s)
- M. Carlu
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif sur Yvette, France
| | - O. Chehab
- Ecole Normale Superieure Paris-Saclay, France
| | - L. Dalla Porta
- Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - D. Depannemaecker
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif sur Yvette, France
| | - C. Héricé
- Strathclyde Institute of Pharmacy and Biomedical Sciences, Glasgow, Scotland, United Kingdom
| | - M. Jedynak
- Université Grenoble Alpes, Grenoble Institut des Neurosciences and Institut National de la Santé et de la Recherche Médicale (INSERM), U1216, France
| | - E. Köksal Ersöz
- INSERM, U1099, Rennes, France
- MathNeuro Team, Inria Sophia Antipolis Méditerranée, Sophia Antipolis, France
| | - P. Muratore
- Physics Department, Sapienza University, Rome, Italy
| | - S. Souihel
- Université Côte d’Azur, Inria Sophia Antipolis Méditerranée, France
| | - C. Capone
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif sur Yvette, France
| | - Y. Zerlaut
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif sur Yvette, France
| | - A. Destexhe
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif sur Yvette, France
| | - M. di Volo
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif sur Yvette, France
- Laboratoire de Physique Théorique et Modelisation, Université de Cergy-Pontoise, Cergy-Pontoise, France
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Nghiem TAE, Tort-Colet N, Górski T, Ferrari U, Moghimyfiroozabad S, Goldman JS, Teleńczuk B, Capone C, Bal T, di Volo M, Destexhe A. Cholinergic Switch between Two Types of Slow Waves in Cerebral Cortex. Cereb Cortex 2020; 30:3451-3466. [DOI: 10.1093/cercor/bhz320] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 01/17/2023] Open
Abstract
Abstract
Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory versus excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases.
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Affiliation(s)
- Trang-Anh E Nghiem
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
- Laboratory of Physics, Department of Physics, Ecole Normale Supérieure, 75005 Paris, France
| | - Núria Tort-Colet
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Tomasz Górski
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Ulisse Ferrari
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 75012 Paris, France
| | - Shayan Moghimyfiroozabad
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Jennifer S Goldman
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Bartosz Teleńczuk
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Cristiano Capone
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
- Istituto Nazionale di Fisica Nucleare Sezione di Roma, 00185 Rome, Italy
| | - Thierry Bal
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Matteo di Volo
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Alain Destexhe
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
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Goldman JS, Tort-Colet N, di Volo M, Susin E, Bouté J, Dali M, Carlu M, Nghiem TA, Górski T, Destexhe A. Bridging Single Neuron Dynamics to Global Brain States. Front Syst Neurosci 2019; 13:75. [PMID: 31866837 PMCID: PMC6908479 DOI: 10.3389/fnsys.2019.00075] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 11/19/2019] [Indexed: 11/13/2022] Open
Abstract
Biological neural networks produce information backgrounds of multi-scale spontaneous activity that become more complex in brain states displaying higher capacities for cognition, for instance, attentive awake versus asleep or anesthetized states. Here, we review brain state-dependent mechanisms spanning ion channel currents (microscale) to the dynamics of brain-wide, distributed, transient functional assemblies (macroscale). Not unlike how microscopic interactions between molecules underlie structures formed in macroscopic states of matter, using statistical physics, the dynamics of microscopic neural phenomena can be linked to macroscopic brain dynamics through mesoscopic scales. Beyond spontaneous dynamics, it is observed that stimuli evoke collapses of complexity, most remarkable over high dimensional, asynchronous, irregular background dynamics during consciousness. In contrast, complexity may not be further collapsed beyond synchrony and regularity characteristic of unconscious spontaneous activity. We propose that increased dimensionality of spontaneous dynamics during conscious states supports responsiveness, enhancing neural networks' emergent capacity to robustly encode information over multiple scales.
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Affiliation(s)
- Jennifer S. Goldman
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Núria Tort-Colet
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Matteo di Volo
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Eduarda Susin
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Jules Bouté
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Melissa Dali
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Mallory Carlu
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | | | - Tomasz Górski
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Alain Destexhe
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
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Destexhe A. Asynchronous irregular states during wakefulness, and Up/Down states during sleep: How to make sense of this activity? IBRO Rep 2019. [DOI: 10.1016/j.ibror.2019.07.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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31
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Amunts K, Knoll AC, Lippert T, Pennartz CMA, Ryvlin P, Destexhe A, Jirsa VK, D’Angelo E, Bjaalie JG. The Human Brain Project-Synergy between neuroscience, computing, informatics, and brain-inspired technologies. PLoS Biol 2019; 17:e3000344. [PMID: 31260438 PMCID: PMC6625714 DOI: 10.1371/journal.pbio.3000344] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 07/12/2019] [Indexed: 02/03/2023] Open
Abstract
The Human Brain Project (HBP) is a European flagship project with a 10-year horizon aiming to understand the human brain and to translate neuroscience knowledge into medicine and technology. To achieve such aims, the HBP explores the multilevel complexity of the brain in space and time; transfers the acquired knowledge to brain-derived applications in health, computing, and technology; and provides shared and open computing tools and data through the HBP European brain research infrastructure. We discuss how the HBP creates a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating perspectives on societal benefits. This Community Page article presents the Human Brain Project; a European Flagship project with a ten-year horizon aiming to understand the human brain and translate neuroscience knowledge into medicine and technology.
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Affiliation(s)
- Katrin Amunts
- Institute for Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Germany
- C. and O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- * E-mail:
| | - Alois C. Knoll
- Institut für Informatik VI, Technische Universität München, Garching bei München, Germany
| | - Thomas Lippert
- Jülich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich, Germany
| | - Cyriel M. A. Pennartz
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, the Netherlands
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Centre Hospitalo-Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Alain Destexhe
- Unité de Neurosciences, Information & Complexité (UNIC), Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
| | - Viktor K. Jirsa
- Institut de Neurosciences des Systèmes, Inserm UMR1106, Aix-Marseille Université, Faculté de Médecine, Marseille, France
| | - Egidio D’Angelo
- Department of Brain and Behavioral Science, Unit of Neurophysiology, University of Pavia, Pavia, Italy
| | - Jan G. Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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Hahn G, Skeide MA, Mantini D, Ganzetti M, Destexhe A, Friederici AD, Deco G. A new computational approach to estimate whole-brain effective connectivity from functional and structural MRI, applied to language development. Sci Rep 2019; 9:8479. [PMID: 31186486 PMCID: PMC6559954 DOI: 10.1038/s41598-019-44909-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 05/23/2019] [Indexed: 12/14/2022] Open
Abstract
Recently introduced effective connectivity methods allow for the in-vivo investigation of large-scale functional interactions between brain regions. However, dynamic causal modeling, the most widely used technique to date, typically captures only a few predefined regions of interest. In this study, we present an alternative computational approach to infer effective connectivity within the entire connectome and show its performance on a developmental cohort with emerging language capacities. The novel approach provides new opportunities to quantify effective connectivity changes in the human brain.
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Affiliation(s)
- Gerald Hahn
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain.
- Unit for Neurosciences, Information and Complexity (UNIC), CNRS, 91190, Gif-sur-Yvette, France and The European Institute for Theoretical Neuroscience (EITN), 75012, Paris, France.
| | - Michael A Skeide
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001, Leuven, Belgium
- Functional Neuroimaging Laboratory, Fondazione Ospedale San Camillo - IRCCS, 30126, Venezia, Italy
| | - Marco Ganzetti
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001, Leuven, Belgium
| | - Alain Destexhe
- Unit for Neurosciences, Information and Complexity (UNIC), CNRS, 91190, Gif-sur-Yvette, France and The European Institute for Theoretical Neuroscience (EITN), 75012, Paris, France
| | - Angela D Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats,Universitat Pompeu Fabra, 08010, Barcelona, Spain
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33
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Einevoll GT, Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M, Migliore M, Ness TV, Plesser HE, Schürmann F. The Scientific Case for Brain Simulations. Neuron 2019; 102:735-744. [DOI: 10.1016/j.neuron.2019.03.027] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 02/06/2019] [Accepted: 03/18/2019] [Indexed: 01/30/2023]
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di Volo M, Romagnoni A, Capone C, Destexhe A. Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation. Neural Comput 2019; 31:653-680. [DOI: 10.1162/neco_a_01173] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Accurate population models are needed to build very large-scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties are involved, such as conductance-based interactions and spike-frequency adaptation. Here, we consider such models based on networks of adaptive exponential integrate-and-fire excitatory and inhibitory neurons. Using a master equation formalism, we derive a mean-field model of such networks and compare it to the full network dynamics. The mean-field model is capable of correctly predicting the average spontaneous activity levels in asynchronous irregular regimes similar to in vivo activity. It also captures the transient temporal response of the network to complex external inputs. Finally, the mean-field model is also able to quantitatively describe regimes where high- and low-activity states alternate (up-down state dynamics), leading to slow oscillations. We conclude that such mean-field models are biologically realistic in the sense that they can capture both spontaneous and evoked activity, and they naturally appear as candidates to build very large-scale models involving multiple brain areas.
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Affiliation(s)
- Matteo di Volo
- Unité de Neuroscience, Information et Complexité, CNRS FRE 3693, 91198 Gif sur Yvette, France
| | - Alberto Romagnoni
- Centre de Recherche sur l'inflammation UMR 1149, Inserm-Université Paris Diderot, 75018 Paris, France, and Data Team, Departement d'informatique de l'Ecole normale supérieure, CNRS, PSL Research University, 75005 Paris, France, and European Institute for Theoretical Neuroscience, 75012 Paris, France
| | - Cristiano Capone
- European Institute for Theoretical Neuroscience, 75012 Paris, France, and INFN Sezione di Roma, Rome 00185, Italy
| | - Alain Destexhe
- Unité de Neuroscience, Information et Complexité, CNRS FRE 3693, 91198 Gif sur Yvette, France, and European Institute for Theoretical Neuroscience, 75012 Paris, France
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35
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Bédard C, Destexhe A. Is the Extracellular Impedance High and Non-resistive in Cerebral Cortex? Biophys J 2019; 113:1639-1642. [PMID: 28978454 DOI: 10.1016/j.bpj.2017.08.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 07/15/2017] [Accepted: 08/14/2017] [Indexed: 11/26/2022] Open
Abstract
A recent commentary to Biophysical Journal criticized a previous study published in the same journal by Gomes et al. in 2016, and an alternative interpretation of the measurements was proposed. We reply here to these criticisms and provide some additional clarification, in particular, about a possible misinterpretation of the electrical circuit corresponding to these experiments. We suggest that, indeed, the extracellular impedance in cerebral cortex could be high and non-resistive, and we propose further experiments to settle this issue.
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Górski T, Veltz R, Galtier M, Fragnaud H, Goldman JS, Teleńczuk B, Destexhe A. Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity. J Comput Neurosci 2018; 45:223-234. [PMID: 30547292 PMCID: PMC6306432 DOI: 10.1007/s10827-018-0707-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 10/30/2018] [Accepted: 11/06/2018] [Indexed: 11/28/2022]
Abstract
Many neurons possess dendrites enriched with sodium channels and are capable of generating action potentials. However, the role of dendritic sodium spikes remain unclear. Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons.
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Affiliation(s)
- Tomasz Górski
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France. .,European Institute for Theoretical Neuroscience, Paris, France.
| | | | - Mathieu Galtier
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Hélissande Fragnaud
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Jennifer S Goldman
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.,European Institute for Theoretical Neuroscience, Paris, France
| | - Bartosz Teleńczuk
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.,European Institute for Theoretical Neuroscience, Paris, France
| | - Alain Destexhe
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.,European Institute for Theoretical Neuroscience, Paris, France
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37
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Nghiem TA, Telenczuk B, Marre O, Destexhe A, Ferrari U. Maximum-entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity. Phys Rev E 2018; 98:012402. [PMID: 30110850 DOI: 10.1103/physreve.98.012402] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Indexed: 01/20/2023]
Abstract
Maximum entropy models can be inferred from large datasets to uncover how collective dynamics emerge from local interactions. Here, such models are employed to investigate neurons recorded by multi-electrode arrays in the human and monkey cortex. Taking advantage of the separation of excitatory and inhibitory neuron types, we construct a model including this distinction. This approach allows us to shed light on differences between excitatory and inhibitory activity across different brain states such as wakefulness and deep sleep, in agreement with previous findings. Additionally, maximum entropy models can also unveil novel features of neuronal interactions, which are found to be dominated by pairwise interactions during wakefulness, but are population-wide during deep sleep. Overall, we demonstrate that maximum entropy models can be useful to analyze datasets with classified neuron types and to reveal the respective roles of excitatory and inhibitory neurons in organizing coherent dynamics in the cerebral cortex.
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Affiliation(s)
- Trang-Anh Nghiem
- Laboratory of Computational Neuroscience, Unité de Neurosciences, Information et Complexité, CNRS, Gif-Sur-Yvette, France
| | - Bartosz Telenczuk
- Laboratory of Computational Neuroscience, Unité de Neurosciences, Information et Complexité, CNRS, Gif-Sur-Yvette, France
| | - Olivier Marre
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France
| | - Alain Destexhe
- Laboratory of Computational Neuroscience, Unité de Neurosciences, Information et Complexité, CNRS, Gif-Sur-Yvette, France
| | - Ulisse Ferrari
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France
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Rubchinsky LL, Ahn S, Klijn W, Cumming B, Yates S, Karakasis V, Peyser A, Woodman M, Diaz-Pier S, Deraeve J, Vassena E, Alexander W, Beeman D, Kudela P, Boatman-Reich D, Anderson WS, Luque NR, Naveros F, Carrillo RR, Ros E, Arleo A, Huth J, Ichinose K, Park J, Kawai Y, Suzuki J, Mori H, Asada M, Oprisan SA, Dave AI, Babaie T, Robinson P, Tabas A, Andermann M, Rupp A, Balaguer-Ballester E, Lindén H, Christensen RK, Nakamura M, Barkat TR, Tosi Z, Beggs J, Lonardoni D, Boi F, Di Marco S, Maccione A, Berdondini L, Jędrzejewska-Szmek J, Dorman DB, Blackwell KT, Bauermeister C, Keren H, Braun J, Dornas JV, Mavritsaki E, Aldrovandi S, Bridger E, Lim S, Brunel N, Buchin A, Kerr CC, Chizhov A, Huberfeld G, Miles R, Gutkin B, Spencer MJ, Meffin H, Grayden DB, Burkitt AN, Davey CE, Tao L, Tiruvadi V, Ali R, Mayberg H, Butera R, Gunay C, Lamb D, Calabrese RL, Doloc-Mihu A, López-Madrona VJ, Matias FS, Pereda E, Mirasso CR, Canals S, Geminiani A, Pedrocchi A, D’Angelo E, Casellato C, Chauhan A, Soman K, Srinivasa Chakravarthy V, Muddapu VR, Chuang CC, Chen NY, Bayati M, Melchior J, Wiskott L, Azizi AH, Diba K, Cheng S, Smirnova EY, Yakimova EG, Chizhov AV, Chen NY, Shih CT, Florescu D, Coca D, Courtiol J, Jirsa VK, Covolan RJM, Teleńczuk B, Kempter R, Curio G, Destexhe A, Parker J, Klishko AN, Prilutsky BI, Cymbalyuk G, Franke F, Hierlemann A, da Silveira RA, Casali S, Masoli S, Rizza M, Rizza MF, Masoli S, Sun Y, Wong W, Farzan F, Blumberger DM, Daskalakis ZJ, Popovych S, Viswanathan S, Rosjat N, Grefkes C, Daun S, Gentiletti D, Suffczynski P, Gnatkovski V, De Curtis M, Lee H, Paik SB, Choi W, Jang J, Park Y, Song JH, Song M, Pallarés V, Gilson M, Kühn S, Insabato A, Deco G, Glomb K, Ponce-Alvarez A, Ritter P, Gilson M, Campo AT, Thiele A, Deeba F, Robinson PA, van Albada SJ, Rowley A, Hopkins M, Schmidt M, Stokes AB, Lester DR, Furber S, Diesmann M, Barri A, Wiechert MT, DiGregorio DA, Dimitrov AG, Vich C, Berg RW, Guillamon A, Ditlevsen S, Cazé RD, Girard B, Doncieux S, Doyon N, Boahen F, Desrosiers P, Laurence E, Doyon N, Dubé LJ, Eleonora R, Durstewitz D, Schmidt D, Mäki-Marttunen T, Krull F, Bettella F, Metzner C, Devor A, Djurovic S, Dale AM, Andreassen OA, Einevoll GT, Næss S, Ness TV, Halnes G, Halgren E, Halnes G, Mäki-Marttunen T, Pettersen KH, Andreassen OA, Sætra MJ, Hagen E, Schiffer A, Grzymisch A, Persike M, Ernst U, Harnack D, Ernst UA, Tomen N, Zucca S, Pasquale V, Pica G, Molano-Mazón M, Chiappalone M, Panzeri S, Fellin T, Oie KS, Boothe DL, Crone JC, Yu AB, Felton MA, Zulfiqar I, Moerel M, De Weerd P, Formisano E, Boothe DL, Crone JC, Felton MA, Oie K, Franaszczuk P, Diggelmann R, Fiscella M, Hierlemann A, Franke F, Guarino D, Antolík J, Davison AP, Frègnac Y, Etienne BX, Frohlich F, Lefebvre J, Marcos E, Mattia M, Genovesio A, Fedorov LA, Dijkstra TM, Sting L, Hock H, Giese MA, Buhry L, Langlet C, Giovannini F, Verbist C, Salvadé S, Giugliano M, Henderson JA, Wernecke H, Sándor B, Gros C, Voges N, Dabrovska P, Riehle A, Brochier T, Grün S, Gu Y, Gong P, Dumont G, Novikov NA, Gutkin BS, Tewatia P, Eriksson O, Kramer A, Santos J, Jauhiainen A, Kotaleski JH, Belić JJ, Kumar A, Kotaleski JH, Shimono M, Hatano N, Ahmad S, Cui Y, Hawkins J, Senk J, Korvasová K, Tetzlaff T, Helias M, Kühn T, Denker M, Mana P, Grün S, Dahmen D, Schuecker J, Goedeke S, Keup C, Goedeke S, Heuer K, Bakker R, Tiesinga P, Toro R, Qin W, Hadjinicolaou A, Grayden DB, Ibbotson MR, Kameneva T, Lytton WW, Mulugeta L, Drach A, Myers JG, Horner M, Vadigepalli R, Morrison T, Walton M, Steele M, Anthony Hunt C, Tam N, Amaducci R, Muñiz C, Reyes-Sánchez M, Rodríguez FB, Varona P, Cronin JT, Hennig MH, Iavarone E, Yi J, Shi Y, Zandt BJ, Van Geit W, Rössert C, Markram H, Hill S, O’Reilly C, Iavarone E, Shi Y, Perin R, Lu H, Zandt BJ, Bryson A, Rössert C, Hadrava M, Hlinka J, Hosaka R, Olenik M, Houghton C, Iannella N, Launey T, Kameneva T, Kotsakidis R, Meffin H, Soriano J, Kubo T, Inoue T, Kida H, Yamakawa T, Suzuki M, Ikeda K, Abbasi S, Hudson AE, Heck DH, Jaeger D, Lee J, Abbasi S, Janušonis S, Saggio ML, Spiegler A, Stacey WC, Bernard C, Lillo D, Bernard C, Petkoski S, Spiegler A, Drakesmith M, Jones DK, Zadeh AS, Kambhampati C, Karbowski J, Kaya ZG, Lakretz Y, Treves A, Li LW, Lizier J, Kerr CC, Masquelier T, Kheradpisheh SR, Kim H, Kim CS, Marakshina JA, Vartanov AV, Neklyudova AA, Kozlovskiy SA, Kiselnikov AA, Taniguchi K, Kitano K, Schmitt O, Lessmann F, Schwanke S, Eipert P, Meinhardt J, Beier J, Kadir K, Karnitzki A, Sellner L, Klünker AC, Kuch L, Ruß F, Jenssen J, Wree A, Sanz-Leon P, Knock SA, Chien SC, Maess B, Knösche TR, Cohen CC, Popovic MA, Klooster J, Kole MH, Roberts EA, Kopell NJ, Kepple D, Giaffar H, Rinberg D, Koulakov A, Forlim CG, Klock L, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Gallinat J, Lee RX, Stephens GJ, Kuhn B, Tauffer L, Isope P, Inoue K, Ohmura Y, Yonekura S, Kuniyoshi Y, Jang HJ, Kwag J, de Kamps M, Lai YM, dos Santos F, Lam KP, Andras P, Imperatore J, Helms J, Tompa T, Lavin A, Inkpen FH, Ashby MC, Lepora NF, Shifman AR, Lewis JE, Zhang Z, Feng Y, Tetzlaff C, Kulvicius T, Li Y, Pena RFO, Bernardi D, Roque AC, Lindner B, Bernardi D, Vellmer S, Saudargiene A, Maninen T, Havela R, Linne ML, Powanwe A, Longtin A, Naveros F, Garrido JA, Graham JW, Dura-Bernal S, Angulo SL, Neymotin SA, Antic SD. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2. BMC Neurosci 2017. [PMCID: PMC5592442 DOI: 10.1186/s12868-017-0371-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Newton AJH, Seidenstein AH, McDougal RA, Pérez-Cervera A, Huguet G, M-Seara T, Haimerl C, Angulo-Garcia D, Torcini A, Cossart R, Malvache A, Skiker K, Maouene M, Ragognetti G, Lorusso L, Viggiano A, Marcelli A, Senatore R, Parziale A, Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés J, Laureys S, Marinazzo D, Stramaglia S, Bassez I, Faes L, Almgren H, Razi A, Van de Steen F, Krebs R, Aerts H, Kanari L, Dlotko P, Scolamiero M, Levi R, Shillcock J, de Kock CP, Hess K, Markram H, Ly C, Marsat G, Gillespie T, Sandström M, Abrams M, Grethe JS, Martone M, De Gernier R, Solinas S, Rössert C, Haelterman M, Massar S, Pasquale V, Pastore VP, Martinoia S, Massobrio P, Capone C, Tort-Colet N, Sanchez-Vives MV, Mattia M, Almasi A, Cloherty SL, Grayden DB, Wong YT, Ibbotson MR, Meffin H, Prince LY, Tsaneva-Atanasova K, Mellor JR, Mazzoni A, Rosa M, Carpaneto J, Romito LM, Priori A, Micera S, Migliore R, Lupascu CA, Franchina F, Bologna LL, Romani A, Saray S, Van Geit W, Káli S, Thomson A, Mercer A, Lange S, Falck J, Muller E, Schürmann F, Todorov D, Capps R, Barnett W, Molkov Y, Devalle F, Pazó D, Montbrió E, Mochol G, Azab H, Hayden BY, Moreno-Bote R, Balasubramani PP, Chakravarthy SV, Muddapu VR, Gheorghiu MD, Mimica B, Withlock J, Mureșan RC, Zick JL, Schultz K, Blackman RK, Chafee MV, Netoff TI, Roberts N, Nagaraj V, Lamperski A, Netoff TI, Grado LL, Johnson MD, Darrow DP, Lonardoni D, Amin H, Di Marco S, Maccione A, Berdondini L, Nieus T, Stimberg M, Goodman DFM, Nowotny T, Koren V, Dragoi V, Obermayer K, Castro S, Fernandez M, El-Deredy W, Xu K, Maidana JP, Orio P, Chen W, Hepburn I, Casalegno F, Devresse A, Ovcharenko A, Pereira F, Delalondre F, De Schutter E, Bratby P, Gallimore AR, Klingbeil G, Zamora C, Zang Y, Crotty P, Palmerduca E, Antonietti A, Casellato C, Erö C, D’Angelo E, Gewaltig MO, Pedrocchi A, Bytschok I, Dold D, Schemmel J, Meier K, Petrovici MA, Shen HA, Surace SC, Pfister JP, Lefebvre B, Marre O, Yger P, Papoutsi A, Park J, Ash R, Smirnakis S, Poirazi P, Felix RA, Dimitrov AG, Portfors C, Daun S, Toth TI, Jędrzejewska-Szmek J, Kabbani N, Blackwel KT, Moezzi B, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Iannella N, Ridding MC, Triesch J, Maex R, Safaryan K, Steuber V, Tang R, Tang YY, Verveyko DV, Brazhe AR, Verisokin AY, Postnov DE, Günay C, Panuccio G, Giugliano M, Prinz AA, Varona P, Rabinovich MI, Denham J, Ranner T, Cohen N, Reva M, Rebola N, Kirizs T, Nusser Z, DiGregorio D, Mavritsaki E, Rentzelas P, Ukani NH, Tomkins A, Yeh CH, Bruning W, Fenichel AL, Zhou Y, Huang YC, Florescu D, Ortiz CL, Richmond P, Lo CC, Coca D, Chiang AS, Lazar AA, Moezzi B, Creaser JL, Lin C, Ashwin P, Brown JT, Ridler T, Levenstein D, Watson BO, Buzsáki G, Rinzel J, Curtu R, Nguyen A, Assadzadeh S, Robinson PA, Sanz-Leon P, Forlim CG, de Almeida LOB, Pinto RD, Rodríguez FB, Lareo Á, Forlim CG, Rodríguez FB, Montero A, Mosqueiro T, Huerta R, Rodriguez FB, Changoluisa V, Rodriguez FB, Cordeiro VL, Ceballos CC, Kamiji NL, Roque AC, Lytton WW, Knox A, Rosenthal JJC, Daun S, Popovych S, Liu L, Wang BA, Tóth TI, Grefkes C, Fink GR, Rosjat N, Perez-Trujillo A, Espinal A, Sotelo-Figueroa MA, Cruz-Aceves I, Rostro-Gonzalez H, Zapotocky M, Hoskovcová M, Kopecká J, Ulmanová O, Růžička E, Gärtner M, Duvarci S, Roeper J, Schneider G, Albert S, Schmack K, Remme M, Schreiber S, Migliore M, Lupascu CA, Bologna LL, Antonel SM, Courcol JD, Schürmann F, Çelikok SU, Navarro-López EM, Şengör NS, Elibol R, Sengor NS, Özdemir MY, Li T, Arleo A, Sheynikhovich D, Nakamura A, Shimono M, Song Y, Park S, Choi I, Jeong J, Shin HS, Sadeh S, Gleeson P, Angus Silver R, Chatzikalymniou AP, Skinner FK, Sanchez-Rodriguez LM, Sotero RC, Hertäg L, Mackwood O, Sprekeler H, Puhlmann S, Weber SN, Higgins D, Naumann LB, Weber SN, Iyer R, Mihalas S, Ticcinelli V, Stankovski T, McClintock PVE, Stefanovska A, Janjić P, Solev D, Seifert G, Kocarev L, Steinhäuser C, Salmasi M, Glasauer S, Stemmler M, Zhang D, Zhang C, Stepanyants A, Goncharenko J, Kros L, Davey N, de Zeeuw C, Hoebeek F, Sinha A, Adams R, Schmuker M, Psarrou M, Schilstra M, Torben-Nielsen B, Metzner C, Schweikard A, Mäki-Marttunen T, Zurowski B, Marinazzo D, Faes L, Stramaglia S, Jordan HOC, Stringer SM, Gajewska-Dendek E, Suffczyński P, Tam N, Zouridakis G, Pollonini L, Tang YY, Asl MM, Valizadeh A, Tass PA, Nold A, Fan W, Konrad S, Endle H, Vogt J, Tchumatchenko T, Herpich J, Tetzlaff C, Luboeinski J, Nachstedt T, Ciba M, Bahmer A, Thielemann C, Kuebler ES, Tauskela JS, Thivierge JP, Bakker R, García-Amado M, Evangelio M, Clascá F, Tiesinga P, Buckley CL, Toyoizumi T, Dubreuil AM, Monasson R, Treves A, Spalla D, Rosay S, Kleberg FI, Wong W, de Oliveira Floriano B, Matsuo T, Uchida T, Dibenedetto D, Uludağ K, Goodarzinick A, Schmidt M, Hilgetag CC, Diesmann M, van Albada SJ, Fauth M, van Rossum M, Reyes-Sánchez M, Amaducci R, Muñiz C, Varona P, Elices I, Arroyo D, Levi R, Cohen B, Chow C, Vattikuti S, Bertolotti E, Burioni R, di Volo M, Vezzani A, Menzat B, Vogels TP, Wagatsuma N, Saha S, Kapoor R, Kerr R, Wagner J, del Molino LCG, Yang GR, Mejias JF, Wang XJ, Song H, Goodliffe J, Luebke J, Weaver CM, Thomas J, Sinha N, Shaju N, Maszczyk T, Jin J, Cash SS, Dauwels J, Brandon Westover M, Karimian M, Moerel M, De Weerd P, Burwick T, Westra RL, Abeysuriya R, Hadida J, Sotiropoulos S, Jbabdi S, Woolrich M, Bensmail C, Wrobel B, Zhou X, Ji Z, Liu X, Xia Y, Wu S, Wang X, Zhang M, Wu S, Ofer N, Shefi O, Yaari G, Carnevale T, Majumdar A, Sivagnanam S, Yoshimoto K, Smirnova EY, Amakhin DV, Malkin SL, Zaitsev AV, Chizhov AV, Zaleshina M, Zaleshin A, Barranca VJ, Zhu G, Skilling QM, Maruyama D, Ognjanovski N, Aton SJ, Zochowski M, Wu J, Aton S, Rich S, Booth V, Budak M, Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Felton MA, Yu AB, Boothe DL, Oie KS, Franaszczuk PJ, Shuvaev SA, Başerdem B, Zador A, Koulakov AA, López-Madrona VJ, Pereda E, Mirasso CR, Canals S, Masoli S, Rongala UB, Mazzoni A, Spanne A, Jorntell H, Oddo CM, Vartanov AV, Neklyudova AK, Kozlovskiy SA, Kiselnikov AA, Marakshina JA, Teleńczuk M, Teleńczuk B, Destexhe A, Kuokkanen PT, Kraemer A, McColgan T, Carr CE, Kempter R. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3. BMC Neurosci 2017. [PMCID: PMC5592441 DOI: 10.1186/s12868-017-0372-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Chemla S, Muller L, Reynaud A, Takerkart S, Destexhe A, Chavane F. Improving voltage-sensitive dye imaging: with a little help from computational approaches. Neurophotonics 2017; 4:031215. [PMID: 28573154 PMCID: PMC5438098 DOI: 10.1117/1.nph.4.3.031215] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 04/24/2017] [Indexed: 05/29/2023]
Abstract
Voltage-sensitive dye imaging (VSDI) is a key neurophysiological recording tool because it reaches brain scales that remain inaccessible to other techniques. The development of this technique from in vitro to the behaving nonhuman primate has only been made possible thanks to the long-lasting, visionary work of Amiram Grinvald. This work has opened new scientific perspectives to the great benefit to the neuroscience community. However, this unprecedented technique remains largely under-utilized, and many future possibilities await for VSDI to reveal new functional operations. One reason why this tool has not been used extensively is the inherent complexity of the signal. For instance, the signal reflects mainly the subthreshold neuronal population response and is not linked to spiking activity in a straightforward manner. Second, VSDI gives access to intracortical recurrent dynamics that are intrinsically complex and therefore nontrivial to process. Computational approaches are thus necessary to promote our understanding and optimal use of this powerful technique. Here, we review such approaches, from computational models to dissect the mechanisms and origin of the recorded signal, to advanced signal processing methods to unravel new neuronal interactions at mesoscopic scale. Only a stronger development of interdisciplinary approaches can bridge micro- to macroscales.
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Affiliation(s)
- Sandrine Chemla
- Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), UMR-7289 Institut de Neurosciences de la Timone, Marseille, France
| | - Lyle Muller
- Salk Institute for Biological Studies, Computational Neurobiology Laboratory, La Jolla, California, United States
| | - Alexandre Reynaud
- McGill University, McGill Vision Research, Department of Ophthalmology, Montreal, Quebec, Canada
| | - Sylvain Takerkart
- Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), UMR-7289 Institut de Neurosciences de la Timone, Marseille, France
| | - Alain Destexhe
- Unit for Neurosciences, Information and Complexity (UNIC), Centre National de la Recherche Scientifique (CNRS), UPR-3293, Gif-sur-Yvette, France
- The European Institute for Theoretical Neuroscience (EITN), Paris, France
| | - Frédéric Chavane
- Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), UMR-7289 Institut de Neurosciences de la Timone, Marseille, France
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Zerlaut Y, Destexhe A. Enhanced Responsiveness and Low-Level Awareness in Stochastic Network States. Neuron 2017; 94:1002-1009. [DOI: 10.1016/j.neuron.2017.04.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 02/27/2017] [Accepted: 04/02/2017] [Indexed: 11/17/2022]
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Zerlaut Y, Destexhe A. Heterogeneous firing responses predict diverse couplings to presynaptic activity in mice layer V pyramidal neurons. PLoS Comput Biol 2017; 13:e1005452. [PMID: 28410418 PMCID: PMC5409182 DOI: 10.1371/journal.pcbi.1005452] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 04/28/2017] [Accepted: 03/10/2017] [Indexed: 11/19/2022] Open
Abstract
In this study, we present a theoretical framework combining experimental characterizations and analytical calculus to capture the firing rate input-output properties of single neurons in the fluctuation-driven regime. Our framework consists of a two-step procedure to treat independently how the dendritic input translates into somatic fluctuation variables, and how the latter determine action potential firing. We use this framework to investigate the functional impact of the heterogeneity in firing responses found experimentally in young mice layer V pyramidal cells. We first design and calibrate in vitro a simplified morphological model of layer V pyramidal neurons with a dendritic tree following Rall's branching rule. Then, we propose an analytical derivation for the membrane potential fluctuations at the soma as a function of the properties of the synaptic input in dendrites. This mathematical description allows us to easily emulate various forms of synaptic input: either balanced, unbalanced, synchronized, purely proximal or purely distal synaptic activity. We find that those different forms of dendritic input activity lead to various impact on the somatic membrane potential fluctuations properties, thus raising the possibility that individual neurons will differentially couple to specific forms of activity as a result of their different firing response. We indeed found such a heterogeneous coupling between synaptic input and firing response for all types of presynaptic activity. This heterogeneity can be explained by different levels of cellular excitability in the case of the balanced, unbalanced, synchronized and purely distal activity. A notable exception appears for proximal dendritic inputs: increasing the input level can either promote firing response in some cells, or suppress it in some other cells whatever their individual excitability. This behavior can be explained by different sensitivities to the speed of the fluctuations, which was previously associated to different levels of sodium channel inactivation and density. Because local network connectivity rather targets proximal dendrites, our results suggest that this aspect of biophysical heterogeneity might be relevant to neocortical processing by controlling how individual neurons couple to local network activity.
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Affiliation(s)
- Yann Zerlaut
- Unité de Neurosciences, Information et Complexité. Centre National de la Recherche Scientifique. 1 avenue de la terrasse, Gif sur Yvette, France
- Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Corso Bettini 31, Rovereto, Italy
| | - Alain Destexhe
- Unité de Neurosciences, Information et Complexité. Centre National de la Recherche Scientifique. 1 avenue de la terrasse, Gif sur Yvette, France
- European Institute for Theoretical Neuroscience. 74 Rue du Faubourg Saint-Antoine, Paris, France
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Bedard C, Gomes JM, Bal T, Destexhe A. A framework to reconcile frequency scaling measurements, from intracellular recordings, local-field potentials, up to EEG and MEG signals. J Integr Neurosci 2017; 16:3-18. [DOI: 10.3233/jin-160001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Abstract
Critical states are sometimes identified experimentally through power-law statistics or universal scaling functions. We show here that such features naturally emerge from networks in self-sustained irregular regimes away from criticality. In these regimes, statistical physics theory of large interacting systems predict a regime where the nodes have independent and identically distributed dynamics. We thus investigated the statistics of a system in which units are replaced by independent stochastic surrogates and found the same power-law statistics, indicating that these are not sufficient to establish criticality. We rather suggest that these are universal features of large-scale networks when considered macroscopically. These results put caution on the interpretation of scaling laws found in nature.
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Affiliation(s)
- Jonathan Touboul
- The Mathematical Neuroscience Laboratory, CIRB/Collège de France (CNRS UMR 7241, INSERM U1050, UPMC ED 158, MEMOLIFE PSL), Paris, France.,MYCENAE Team, INRIA, Paris, France
| | - Alain Destexhe
- Unit for Neurosciences, Information and Complexity (UNIC), CNRS, Gif sur Yvette, France.,The European Institute for Theoretical Neuroscience (EITN), Paris, France
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Teleńczuk B, Dehghani N, Le Van Quyen M, Cash SS, Halgren E, Hatsopoulos NG, Destexhe A. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Sci Rep 2017; 7:40211. [PMID: 28074856 PMCID: PMC5225490 DOI: 10.1038/srep40211] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 12/05/2016] [Indexed: 01/11/2023] Open
Abstract
The local field potential (LFP) is generated by large populations of neurons, but unitary contribution of spiking neurons to LFP is not well characterised. We investigated this contribution in multi-electrode array recordings from human and monkey neocortex by examining the spike-triggered LFP average (st-LFP). The resulting st-LFPs were dominated by broad spatio-temporal components due to ongoing activity, synaptic inputs and recurrent connectivity. To reduce the spatial reach of the st-LFP and observe the local field related to a single spike we applied a spatial filter, whose weights were adapted to the covariance of ongoing LFP. The filtered st-LFPs were limited to the perimeter of 800 μm around the neuron, and propagated at axonal speed, which is consistent with their unitary nature. In addition, we discriminated between putative inhibitory and excitatory neurons and found that the inhibitory st-LFP peaked at shorter latencies, consistently with previous findings in hippocampal slices. Thus, in human and monkey neocortex, the LFP reflects primarily inhibitory neuron activity.
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Affiliation(s)
- Bartosz Teleńczuk
- Unité de Neurosciences, Information &Complexité, Centre National de la Recherche Scientifique, 91198 Gif-sur-Yvette, France
| | - Nima Dehghani
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, USA.,New England Complex Systems Institute, Cambridge, USA
| | - Michel Le Van Quyen
- L'Institut du Cerveau et de la Moelle Épinière, UMRS 1127, CNRS UMR 7225, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Eric Halgren
- Multimodal Imaging Laboratory, Departments of Neurosciences and Radiology, University of California San Diego, USA
| | - Nicholas G Hatsopoulos
- Department of Organismal Biology and Anatomy, Committee on Computational Neuroscience, University of Chicago, USA
| | - Alain Destexhe
- Unité de Neurosciences, Information &Complexité, Centre National de la Recherche Scientifique, 91198 Gif-sur-Yvette, France
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Gomes JM, Bédard C, Valtcheva S, Nelson M, Khokhlova V, Pouget P, Venance L, Bal T, Destexhe A. Intracellular Impedance Measurements Reveal Non-ohmic Properties of the Extracellular Medium around Neurons. Biophys J 2016; 110:234-46. [PMID: 26745426 DOI: 10.1016/j.bpj.2015.11.019] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 10/26/2015] [Accepted: 11/10/2015] [Indexed: 10/22/2022] Open
Abstract
Determining the electrical properties of the extracellular space around neurons is important for understanding the genesis of extracellular potentials, as well as for localizing neuronal activity from extracellular recordings. However, the exact nature of these extracellular properties is still uncertain. Here, we introduce a method to measure the impedance of the tissue, one that preserves the intact cell-medium interface using whole-cell patch-clamp recordings in vivo and in vitro. We find that neural tissue has marked non-ohmic and frequency-filtering properties, which are not consistent with a resistive (ohmic) medium, as often assumed. The amplitude and phase profiles of the measured impedance are consistent with the contribution of ionic diffusion. We also show that the impact of such frequency-filtering properties is possibly important on the genesis of local field potentials, as well as on the cable properties of neurons. These results show non-ohmic properties of the extracellular medium around neurons, and suggest that source estimation methods, as well as the cable properties of neurons, which all assume ohmic extracellular medium, may need to be reevaluated.
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Affiliation(s)
- Jean-Marie Gomes
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Claude Bédard
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Silvana Valtcheva
- Centre Interdisciplinaire de Recherche en Biologie, Centre National de la Recherche Scientifique UMR 7241, Institut National de la Santé et de la Recherche Médicale U1050, Collège de France, Paris, France
| | - Matthew Nelson
- Institut du Cerveau et de la Moelle Epinière, Centre National de la Recherche Scientifique UMR 7225, Institut National de la Santé et de la Recherche Médicale UMRS 975, Hôpital de la Salpétrière, Université Pierre et Marie Curie, Paris, France
| | - Vitalia Khokhlova
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Pierre Pouget
- Institut du Cerveau et de la Moelle Epinière, Centre National de la Recherche Scientifique UMR 7225, Institut National de la Santé et de la Recherche Médicale UMRS 975, Hôpital de la Salpétrière, Université Pierre et Marie Curie, Paris, France
| | - Laurent Venance
- Centre Interdisciplinaire de Recherche en Biologie, Centre National de la Recherche Scientifique UMR 7241, Institut National de la Santé et de la Recherche Médicale U1050, Collège de France, Paris, France
| | - Thierry Bal
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Alain Destexhe
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.
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Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, Levi R, Rodriguez FB, Varona P, Hwang E, Kim B, Han HB, Kim T, McKenna JT, Brown RE, McCarley RW, Choi JH, Rankin J, Popp PO, Rinzel J, Tabas A, Rupp A, Balaguer-Ballester E, Maturana MI, Grayden DB, Cloherty SL, Kameneva T, Ibbotson MR, Meffin H, Koren V, Lochmann T, Dragoi V, Obermayer K, Psarrou M, Schilstra M, Davey N, Torben-Nielsen B, Steuber V, Ju H, Yu J, Hines ML, Chen L, Yu Y, Kim J, Leahy W, Shlizerman E, Birgiolas J, Gerkin RC, Crook SM, Viriyopase A, Memmesheimer RM, Gielen S, Dabaghian Y, DeVito J, Perotti L, Kim AJ, Fenk LM, Cheng C, Maimon G, Zhao C, Widmer Y, Sprecher S, Senn W, Halnes G, Mäki-Marttunen T, Keller D, Pettersen KH, Andreassen OA, Einevoll GT, Yamada Y, Steyn-Ross ML, Alistair Steyn-Ross D, Mejias JF, Murray JD, Kennedy H, Wang XJ, Kruscha A, Grewe J, Benda J, Lindner B, Badel L, Ohta K, Tsuchimoto Y, Kazama H, Kahng B, Tam ND, Pollonini L, Zouridakis G, Soh J, Kim D, Yoo M, Palmer SE, Culmone V, Bojak I, Ferrario A, Merrison-Hort R, Borisyuk R, Kim CS, Tezuka T, Joo P, Rho YA, Burton SD, Bard Ermentrout G, Jeong J, Urban NN, Marsalek P, Kim HH, Moon SH, Lee DW, Lee SB, Lee JY, Molkov YI, Hamade K, Teka W, Barnett WH, Kim T, Markin S, Rybak IA, Forro C, Dermutz H, Demkó L, Vörös J, Babichev A, Huang H, Verduzco-Flores S, Dos Santos F, Andras P, Metzner C, Schweikard A, Zurowski B, Roach JP, Sander LM, Zochowski MR, Skilling QM, Ognjanovski N, Aton SJ, Zochowski M, Wang SJ, Ouyang G, Guang J, Zhang M, Michael Wong KY, Zhou C, Robinson PA, Sanz-Leon P, Drysdale PM, Fung F, Abeysuriya RG, Rennie CJ, Zhao X, Choe Y, Yang HF, Mi Y, Lin X, Wu S, Liedtke J, Schottdorf M, Wolf F, Yamamura Y, Wickens JR, Rumbell T, Ramsey J, Reyes A, Draguljić D, Hof PR, Luebke J, Weaver CM, He H, Yang X, Ma H, Xu Z, Wang Y, Baek K, Morris LS, Kundu P, Voon V, Agnes EJ, Vogels TP, Podlaski WF, Giese M, Kuravi P, Vogels R, Seeholzer A, Podlaski W, Ranjan R, Vogels T, Torres JJ, Baroni F, Latorre R, Gips B, Lowet E, Roberts MJ, de Weerd P, Jensen O, van der Eerden J, Goodarzinick A, Niry MD, Valizadeh A, Pariz A, Parsi SS, Warburton JM, Marucci L, Tamagnini F, Brown J, Tsaneva-Atanasova K, Kleberg FI, Triesch J, Moezzi B, Iannella N, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Ridding MC, Zapotocky M, Smit D, Fouquet C, Trembleau A, Dasgupta S, Nishikawa I, Aihara K, Toyoizumi T, Robb DT, Mellen N, Toporikova N, Tang R, Tang YY, Liang G, Kiser SA, Howard JH, Goncharenko J, Voronenko SO, Ahamed T, Stephens G, Yger P, Lefebvre B, Spampinato GLB, Esposito E, et Olivier Marre MS, Choi H, Song MH, Chung S, Lee DD, Sompolinsky H, Phillips RS, Smith J, Chatzikalymniou AP, Ferguson K, Alex Cayco Gajic N, Clopath C, Angus Silver R, Gleeson P, Marin B, Sadeh S, Quintana A, Cantarelli M, Dura-Bernal S, Lytton WW, Davison A, Li L, Zhang W, Wang D, Song Y, Park S, Choi I, Shin HS, Choi H, Pasupathy A, Shea-Brown E, Huh D, Sejnowski TJ, Vogt SM, Kumar A, Schmidt R, Van Wert S, Schiff SJ, Veale R, Scheutz M, Lee SW, Gallinaro J, Rotter S, Rubchinsky LL, Cheung CC, Ratnadurai-Giridharan S, Shomali SR, Ahmadabadi MN, Shimazaki H, Nader Rasuli S, Zhao X, Rasch MJ, Wilting J, Priesemann V, Levina A, Rudelt L, Lizier JT, Spinney RE, Rubinov M, Wibral M, Bak JH, Pillow J, Zaho Y, Park IM, Kang J, Park HJ, Jang J, Paik SB, Choi W, Lee C, Song M, Lee H, Park Y, Yilmaz E, Baysal V, Ozer M, Saska D, Nowotny T, Chan HK, Diamond A, Herrmann CS, Murray MM, Ionta S, Hutt A, Lefebvre J, Weidel P, Duarte R, Morrison A, Lee JH, Iyer R, Mihalas S, Koch C, Petrovici MA, Leng L, Breitwieser O, Stöckel D, Bytschok I, Martel R, Bill J, Schemmel J, Meier K, Esler TB, Burkitt AN, Kerr RR, Tahayori B, Nolte M, Reimann MW, Muller E, Markram H, Parziale A, Senatore R, Marcelli A, Skiker K, Maouene M, Neymotin SA, Seidenstein A, Lakatos P, Sanger TD, Menzies RJ, McLauchlan C, van Albada SJ, Kedziora DJ, Neymotin S, Kerr CC, Suter BA, Shepherd GMG, Ryu J, Lee SH, Lee J, Lee HJ, Lim D, Wang J, Lee H, Jung N, Anh Quang L, Maeng SE, Lee TH, Lee JW, Park CH, Ahn S, Moon J, Choi YS, Kim J, Jun SB, Lee S, Lee HW, Jo S, Jun E, Yu S, Goetze F, Lai PY, Kim S, Kwag J, Jang HJ, Filipović M, Reig R, Aertsen A, Silberberg G, Bachmann C, Buttler S, Jacobs H, Dillen K, Fink GR, Kukolja J, Kepple D, Giaffar H, Rinberg D, Shea S, Koulakov A, Bahuguna J, Tetzlaff T, Kotaleski JH, Kunze T, Peterson A, Knösche T, Kim M, Kim H, Park JS, Yeon JW, Kim SP, Kang JH, Lee C, Spiegler A, Petkoski S, Palva MJ, Jirsa VK, Saggio ML, Siep SF, Stacey WC, Bernar C, Choung OH, Jeong Y, Lee YI, Kim SH, Jeong M, Lee J, Kwon J, Kralik JD, Jahng J, Hwang DU, Kwon JH, Park SM, Kim S, Kim H, Kim PS, Yoon S, Lim S, Park C, Miller T, Clements K, Ahn S, Ji EH, Issa FA, Baek J, Oba S, Yoshimoto J, Doya K, Ishii S, Mosqueiro TS, Strube-Bloss MF, Smith B, Huerta R, Hadrava M, Hlinka J, Bos H, Helias M, Welzig CM, Harper ZJ, Kim WS, Shin IS, Baek HM, Han SK, Richter R, Vitay J, Beuth F, Hamker FH, Toppin K, Guo Y, Graham BP, Kale PJ, Gollo LL, Stern M, Abbott LF, Fedorov LA, Giese MA, Ardestani MH, Faraji MJ, Preuschoff K, Gerstner W, van Gendt MJ, Briaire JJ, Kalkman RK, Frijns JHM, Lee WH, Frangou S, Fulcher BD, Tran PHP, Fornito A, Gliske SV, Lim E, Holman KA, Fink CG, Kim JS, Mu S, Briggman KL, Sebastian Seung H, Wegener D, Bohnenkamp L, Ernst UA, Devor A, Dale AM, Lines GT, Edwards A, Tveito A, Hagen E, Senk J, Diesmann M, Schmidt M, Bakker R, Shen K, Bezgin G, Hilgetag CC, van Albada SJ, Sun H, Sourina O, Huang GB, Klanner F, Denk C, Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G, Witek MAG, Clarke EF, Hansen M, Wallentin M, Kringelbach ML, Vuust P, Klingbeil G, De Schutter E, Chen W, Zang Y, Hong S, Takashima A, Zamora C, Gallimore AR, Goldschmidt D, Manoonpong P, Karoly PJ, Freestone DR, Soundry D, Kuhlmann L, Paninski L, Cook M, Lee J, Fishman YI, Cohen YE, Roberts JA, Cocchi L, Sweeney Y, Lee S, Jung WS, Kim Y, Jung Y, Song YK, Chavane F, Soman K, Muralidharan V, Srinivasa Chakravarthy V, Shivkumar S, Mandali A, Pragathi Priyadharsini B, Mehta H, Davey CE, Brinkman BAW, Kekona T, Rieke F, Buice M, De Pittà M, Berry H, Brunel N, Breakspear M, Marsat G, Drew J, Chapman PD, Daly KC, Bradle SP, Seo SB, Su J, Kavalali ET, Blackwell J, Shiau L, Buhry L, Basnayake K, Lee SH, Levy BA, Baker CI, Leleu T, Philips RT, Chhabria K. 25th Annual Computational Neuroscience Meeting: CNS-2016. BMC Neurosci 2016; 17 Suppl 1:54. [PMID: 27534393 PMCID: PMC5001212 DOI: 10.1186/s12868-016-0283-6] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber O8 Analytic solution of cable energy function for cortical axons and dendrites Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network Jimin Kim, Will Leahy, Eli Shlizerman O10 Is the model any good? Objective criteria for computational neuroscience model selection Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook O11 Cooperation and competition of gamma oscillation mechanisms Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen O12 A discrete structure of the brain waves Yuri Dabaghian, Justin DeVito, Luca Perotti O13 Direction-specific silencing of the Drosophila gaze stabilization system Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon O14 What does the fruit fly think about values? A model of olfactory associative learning Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn O15 Effects of ionic diffusion on power spectra of local field potentials (LFP) Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits Yasunori Yamada O17 Spatial coarse-graining the brain: origin of minicolumns Moira L. Steyn-Ross, D. Alistair Steyn-Ross O18 Modeling large-scale cortical networks with laminar structure Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang O19 Information filtering by partial synchronous spikes in a neural population Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner O20 Decoding context-dependent olfactory valence in Drosophila Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama P1 Neural network as a scale-free network: the role of a hub B. Kahng P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging Nicoladie D. Tam P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique Nicoladie D.Tam, Luca Pollonini, George Zouridakis P4 Modeling jamming avoidance of weakly electric fish Jaehyun Soh, DaeEun Kim P5 Synergy and redundancy of retinal ganglion cells in prediction Minsu Yoo, S. E. Palmer P6 A neural field model with a third dimension representing cortical depth Viviana Culmone, Ingo Bojak P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk P8 The recognition dynamics in the brain Chang Sub Kim P9 Multivariate spike train analysis using a positive definite kernel Taro Tezuka P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia Pangyu Joo P11 The ionic basis of heterogeneity affects stochastic synchrony Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban P12 Circular statistics of noise in spike trains with a periodic component Petr Marsalek P14 Representations of directions in EEG-BCI using Gaussian readouts Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong P15 Action selection and reinforcement learning in basal ganglia during reaching movements Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak P17 Axon guidance: modeling axonal growth in T-Junction assay Csaba Forro, Harald Dermutz, László Demkó, János Vörös P19 Transient cell assembly networks encode persistent spatial memories Yuri Dabaghian, Andrey Babichev P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons Haiping Huang P21 Design of biologically-realistic simulations for motor control Sergio Verduzco-Flores P22 Towards understanding the functional impact of the behavioural variability of neurons Filipa Dos Santos, Peter Andras P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia Christoph Metzner, Achim Schweikard, Bartosz Zurowski P24 Memory recall and spike frequency adaptation James P. Roach, Leonard M. Sander, Michal R. Zochowski P25 Stability of neural networks and memory consolidation preferentially occur near criticality Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou P27 Neurofield: a C++ library for fast simulation of 2D neural field models Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao P28 Action-based grounding: Beyond encoding/decoding in neural code Yoonsuck Choe, Huei-Fang Yang P29 Neural computation in a dynamical system with multiple time scales Yuanyuan Mi, Xiaohan Lin, Si Wu P30 Maximum entropy models for 3D layouts of orientation selectivity Joscha Liedtke, Manuel Schottdorf, Fred Wolf P31 A behavioral assay for probing computations underlying curiosity in rodents Yoriko Yamamura, Jeffery R. Wickens P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon P35 Dynamics of cooperative excitatory and inhibitory plasticity Everton J. Agnes, Tim P. Vogels P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons William F. Podlaski, Tim P. Vogels P37 Phenomenological neural model for adaptation of neurons in area IT Martin Giese, Pradeep Kuravi, Rufin Vogels P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona P40 Different roles for transient and sustained activity during active visual processing Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh P42 High frequency neuron can facilitate propagation of signal in neural networks Aref Pariz, Shervin S. Parsi, Alireza Valizadeh P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP Florence I. Kleberg, Jochen Triesch P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch P46 Structure and dynamics of axon network formed in primary cell culture Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau P47 Efficient signal processing and sampling in random networks that generate variability Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi P48 Modeling the effect of riluzole on bursting in respiratory neural networks Daniel T. Robb, Nick Mellen, Natalia Toporikova P49 Mapping relaxation training using effective connectivity analysis Rongxiang Tang, Yi-Yuan Tang P50 Modeling neuron oscillation of implicit sequence learning Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber P52 Nonlinear response of noisy neurons Sergej O. Voronenko, Benjamin Lindner P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion Tosif Ahamed, Greg Stephens P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre P55 Sufficient sampling rates for fast hand motion tracking Hansol Choi, Min-Ho Song P56 Linear readout of object manifolds SueYeon Chung, Dan D. Lee, Haim Sompolinsky P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves Ryan S. Phillips, Jeffrey Smith P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver P60 A set of curated cortical models at multiple scales on Open Source Brain Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver P61 A synaptic story of dynamical information encoding in neural adaptation Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu P62 Physical modeling of rule-observant rodent behavior Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes Hannah Choi, Anitha Pasupathy, Eric Shea-Brown P65 Stability of FORCE learning on spiking and rate-based networks Dongsung Huh, Terrence J. Sejnowski P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments Simon M. Vogt, Arvind Kumar, Robert Schmidt P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation Stephen Van Wert, Steven J. Schiff P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo Richard Veale, Matthias Scheutz P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions Sang Wan Lee P70 Maturation of sensory networks through homeostatic structural plasticity Júlia Gallinaro, Stefan Rotter P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings Paula Sanz-Leon, Peter A. Robinson P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan P73 Exact spike-timing distribution reveals higher-order interactions of neurons Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli P74 Neural mechanism of visual perceptual learning using a multi-layered neural network Xiaochen Zhao, Malte J. Rasch P75 Inferring collective spiking dynamics from mostly unobserved systems Jens Wilting, Viola Priesemann P76 How to infer distributions in the brain from subsampled observations Anna Levina, Viola Priesemann P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons Lucas Rudelt, Joseph T. Lizier, Viola Priesemann P78 A nearest-neighbours based estimator for transfer entropy between spike trains Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann P79 Active learning of psychometric functions with multinomial logistic models Ji Hyun Bak, Jonathan Pillow P81 Inferring low-dimensional network dynamics with variational latent Gaussian process Yuan Zaho, Il Memming Park P82 Computational investigation of energy landscapes in the resting state subcortical brain network Jiyoung Kang, Hae-Jeong Park P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map Jaeson Jang, Se-Bum Paik P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition Woochul Choi, Se-Bum Paik P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map Changju Lee, Jaeson Jang, Se-Bum Paik P86 Computational method classifying neural network activity patterns for imaging data Min Song, Hyeonsu Lee, Se-Bum Paik P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory Youngjin Park, Woochul Choi, Se-Bum Paik P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons Ergin Yilmaz, Veli Baysal, Mahmut Ozer P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance Veronika Koren, Klaus Obermayer P90 Methods for building accurate models of individual neurons Daniel Saska, Thomas Nowotny P91 A full size mathematical model of the early olfactory system of honeybees Ho Ka Chan, Alan Diamond, Thomas Nowotny P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input Philipp Weidel, Renato Duarte, Abigail Morrison P94 Modulation of tuning induced by abrupt reduction of SST cell activity Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas P95 The functional role of VIP cell activation during locomotion Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas P96 Stochastic inference with spiking neural networks Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier P97 Modeling orientation-selective electrical stimulation with retinal prostheses Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin P98 Ion channel noise can explain firing correlation in auditory nerves Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram P100 On the representation of arm reaching movements: a computational model Antonio Parziale, Rosa Senatore, Angelo Marcelli P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior Rosa Senatore, Antonio Parziale, Angelo Marcelli P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge K. Skiker, M. Maouene P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton P104 Effect of network size on computational capacity Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas Juhyoung Ryu, Sang-Hun Lee P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception Joonwon Lee, Sang-Hun Lee P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making Hyang Jung Lee, Sang-Hun Lee P110 A Bayesian algorithm for phoneme Perception and its neural implementation Daeseob Lim, Sang-Hun Lee P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol Jisung Wang, Heonsoo Lee P112 Self-organized criticality of neural avalanche in a neural model on complex networks Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee P114 Computational model to replicate seizure suppression effect by electrical stimulation Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy Felix Goetze, Pik-Yin Lai P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell Seonghyun Kim, Jeehyun Kwag P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model Hyun Jae Jang, Jeehyun Kwag P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison P120 Learning sparse representations in the olfactory bulb Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov P121 Functional classification of homologous basal-ganglia networks Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski P122 Short term memory based on multistability Tim Kunze, Andre Peterson, Thomas Knösche P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units Minjung Kim, Hojeong Kim P125 Decoding laser-induced somatosensory information from EEG Ji Sung Park, Ji Won Yeon, Sung-Phil Kim P126 Phase synchronization of alpha activity for EEG-based personal authentication Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa P130 Epileptic seizures in the unfolding of a codimension-3 singularity Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa P131 Incremental dimensional exploratory reasoning under multi-dimensional environment Oh-hyeon Choung, Yong Jeong P132 A low-cost model of eye movements and memory in personal visual cognition Yong-il Lee, Jaeseung Jeong P133 Complex network analysis of structural connectome of autism spectrum disorder patients Su Hyun Kim, Mir Jeong, Jaeseung Jeong P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong P136 Detecting purchase decision based on hyperfrontality of the EEG Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it? Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta P146 Swinging networks Michal Hadrava, Jaroslav Hlinka P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions Hannah Bos, Moritz Helias P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning Charles M. Welzig, Zachary J. Harper P149 Multiscale complexity analysis for the segmentation of MRI images Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han P150 A neuro-computational model of emotional attention René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker P151 Multi-site delayed feedback stimulation in parkinsonian networks Kelly Toppin, Yixin Guo P152 Bistability in Hodgkin–Huxley-type equations Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency Mark D. McDonnell, Bruce P. Graham P154 Quantifying resilience patterns in brain networks: the importance of directionality Penelope J. Kale, Leonardo L. Gollo P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations Merav Stern, L. F. Abbott P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues Leonid A. Fedorov, Martin A. Giese P157 Spiking model for the interaction between action recognition and action execution Mohammad Hovaidi Ardestani, Martin Giese P158 Surprise-modulated belief update: how to learn within changing environments? Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks Won Hee Lee, Sophia Frangou P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. 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Zerlaut Y, Teleńczuk B, Deleuze C, Bal T, Ouanounou G, Destexhe A. Heterogeneous firing rate response of mouse layer V pyramidal neurons in the fluctuation-driven regime. J Physiol 2016; 594:3791-808. [PMID: 27146816 DOI: 10.1113/jp272317] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 04/05/2016] [Indexed: 11/08/2022] Open
Abstract
KEY POINTS We recreated in vitro the fluctuation-driven regime observed at the soma during asynchronous network activity in vivo and we studied the firing rate response as a function of the properties of the membrane potential fluctuations. We provide a simple analytical template that captures the firing response of both pyramidal neurons and various theoretical models. We found a strong heterogeneity in the firing rate response of layer V pyramidal neurons: in particular, individual neurons differ not only in their mean excitability level, but also in their sensitivity to fluctuations. Theoretical modelling suggest that this observed heterogeneity might arise from various expression levels of the following biophysical properties: sodium inactivation, density of sodium channels and spike frequency adaptation. ABSTRACT Characterizing the input-output properties of neocortical neurons is of crucial importance for understanding the properties emerging at the network level. In the regime of low-rate irregular firing (such as in the awake state), determining those properties for neocortical cells remains, however, both experimentally and theoretically challenging. Here, we studied this problem using a combination of theoretical modelling and in vitro experiments. We first identified, theoretically, three somatic variables that describe the dynamical state at the soma in this fluctuation-driven regime: the mean, standard deviation and time constant of the membrane potential fluctuations. Next, we characterized the firing rate response of individual layer V pyramidal cells in this three-dimensional space by means of perforated-patch recordings and dynamic clamp in the visual cortex of juvenile mice in vitro. We found that individual neurons strongly differ not only in terms of their excitability, but also, and unexpectedly, in their sensitivities to fluctuations. Finally, using theoretical modelling, we attempted to reproduce these results. The model predicts that heterogeneous levels of biophysical properties such as sodium inactivation, sharpness of sodium activation and spike frequency adaptation account for the observed diversity of firing rate responses. Because the firing rate response will determine population rate dynamics during asynchronous neocortical activity, our results show that cortical populations are functionally strongly inhomogeneous in young mouse visual cortex, which should have important consequences on the strategies of cortical computation at early stages of sensory processing.
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Affiliation(s)
- Y Zerlaut
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, FRE 3693, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France.,European Institute for Theoretical Neuroscience, 74 Rue du Faubourg Saint-Antoine, 75012, Paris, France
| | - B Teleńczuk
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, FRE 3693, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France.,European Institute for Theoretical Neuroscience, 74 Rue du Faubourg Saint-Antoine, 75012, Paris, France
| | - C Deleuze
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, FRE 3693, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - T Bal
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, FRE 3693, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - G Ouanounou
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, FRE 3693, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - A Destexhe
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, FRE 3693, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France.,European Institute for Theoretical Neuroscience, 74 Rue du Faubourg Saint-Antoine, 75012, Paris, France
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Zerlaut Y, Ouanounou G, Telenczuk B, Deleuze C, Bal T, Destexhe A. Firing rate response of neocortical neurons in the fluctuation-driven regime. BMC Neurosci 2015. [PMCID: PMC4697666 DOI: 10.1186/1471-2202-16-s1-p59] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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