1
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Yang X, Camera GL. Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570692. [PMID: 38106233 PMCID: PMC10723399 DOI: 10.1101/2023.12.07.570692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.
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2
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Castaldo F, Páscoa Dos Santos F, Timms RC, Cabral J, Vohryzek J, Deco G, Woolrich M, Friston K, Verschure P, Litvak V. Multi-modal and multi-model interrogation of large-scale functional brain networks. Neuroimage 2023; 277:120236. [PMID: 37355200 PMCID: PMC10958139 DOI: 10.1016/j.neuroimage.2023.120236] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/26/2023] Open
Abstract
Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.
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Affiliation(s)
- Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Francisco Páscoa Dos Santos
- Eodyne Systems SL, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom
| | - Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom; Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Paul Verschure
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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3
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Pressey JC, de Saint-Rome M, Raveendran VA, Woodin MA. Chloride transporters controlling neuronal excitability. Physiol Rev 2023; 103:1095-1135. [PMID: 36302178 DOI: 10.1152/physrev.00025.2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Synaptic inhibition plays a crucial role in regulating neuronal excitability, which is the foundation of nervous system function. This inhibition is largely mediated by the neurotransmitters GABA and glycine that activate Cl--permeable ion channels, which means that the strength of inhibition depends on the Cl- gradient across the membrane. In neurons, the Cl- gradient is primarily mediated by two secondarily active cation-chloride cotransporters (CCCs), NKCC1 and KCC2. CCC-mediated regulation of the neuronal Cl- gradient is critical for healthy brain function, as dysregulation of CCCs has emerged as a key mechanism underlying neurological disorders including epilepsy, neuropathic pain, and autism spectrum disorder. This review begins with an overview of neuronal chloride transporters before explaining the dependent relationship between these CCCs, Cl- regulation, and inhibitory synaptic transmission. We then discuss the evidence for how CCCs can be regulated, including by activity and their protein interactions, which underlie inhibitory synaptic plasticity. For readers who may be interested in conducting experiments on CCCs and neuronal excitability, we have included a section on techniques for estimating and recording intracellular Cl-, including their advantages and limitations. Although the focus of this review is on neurons, we also examine how Cl- is regulated in glial cells, which in turn regulate neuronal excitability through the tight relationship between this nonneuronal cell type and synapses. Finally, we discuss the relatively extensive and growing literature on how CCC-mediated neuronal excitability contributes to neurological disorders.
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Affiliation(s)
- Jessica C Pressey
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Miranda de Saint-Rome
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Vineeth A Raveendran
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Melanie A Woodin
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
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4
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Chen S, Yang Q, Lim S. Efficient inference of synaptic plasticity rule with Gaussian process regression. iScience 2023; 26:106182. [PMID: 36879810 PMCID: PMC9985048 DOI: 10.1016/j.isci.2023.106182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.
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Affiliation(s)
- Shirui Chen
- Department of Applied Mathematics, University of Washington, Lewis Hall 201, Box 353925, Seattle, WA 98195-3925, USA.,Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Qixin Yang
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, The Suzanne and Charles Goodman Brain Sciences Building, Edmond J. Safra Campus, Jerusalem, 9190401, Israel.,Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Sukbin Lim
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China.,NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, 3663 Zhongshan Road North, Shanghai, 200062, China
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5
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Hirai H, Sakaba T, Hashimotodani Y. Subcortical glutamatergic inputs exhibit a Hebbian form of long-term potentiation in the dentate gyrus. Cell Rep 2022; 41:111871. [PMID: 36577371 DOI: 10.1016/j.celrep.2022.111871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/19/2022] [Accepted: 12/01/2022] [Indexed: 12/28/2022] Open
Abstract
The hippocampus receives glutamatergic and GABAergic inputs from subcortical regions. Despite the important roles of these subcortical inputs in the regulation of hippocampal circuit, it has not been explored whether associative activation of the subcorticohippocampal pathway induces Hebbian plasticity of subcortical inputs. Here, we demonstrate that the hypothalamic supramammillary nucleus (SuM) to the dentate granule cell (GC) synapses, which co-release glutamate and GABA, undergo associative long-term potentiation (LTP) of glutamatergic, but not GABAergic, co-transmission. This LTP is induced by pairing of SuM inputs with GC spikes. We found that this Hebbian LTP is input-specific, requires NMDA receptors and CaMKII activation, and is expressed postsynaptically. By the net increase in excitatory drive of SuM inputs following LTP induction, associative inputs of SuM and the perforant path effectively discharge GCs. Our results highlight the important role of associative plasticity at SuM-GC synapses in the regulation of dentate gyrus activity and for the encoding of SuM-related information.
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Affiliation(s)
- Himawari Hirai
- Graduate School of Brain Science, Doshisha University, Kyoto 610-0394, Japan
| | - Takeshi Sakaba
- Graduate School of Brain Science, Doshisha University, Kyoto 610-0394, Japan
| | - Yuki Hashimotodani
- Graduate School of Brain Science, Doshisha University, Kyoto 610-0394, Japan.
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6
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Miehl C, Gjorgjieva J. Stability and learning in excitatory synapses by nonlinear inhibitory plasticity. PLoS Comput Biol 2022; 18:e1010682. [PMID: 36459503 PMCID: PMC9718420 DOI: 10.1371/journal.pcbi.1010682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/25/2022] [Indexed: 12/03/2022] Open
Abstract
Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.
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Affiliation(s)
- Christoph Miehl
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
- * E-mail: (CM); (JG)
| | - Julijana Gjorgjieva
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
- * E-mail: (CM); (JG)
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7
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Afferni P, Cascino-Milani F, Mattera A, Baldassarre G. A neuro-inspired computational model of life-long learning and catastrophic interference, mimicking hippocampus novelty-based dopamine modulation and lateral inhibitory plasticity. Front Comput Neurosci 2022; 16:954847. [PMID: 36157843 PMCID: PMC9500484 DOI: 10.3389/fncom.2022.954847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The human brain has a remarkable lifelong learning capability to acquire new experiences while retaining previously acquired information. Several hypotheses have been proposed to explain this capability, but the underlying mechanisms are still unclear. Here, we propose a neuro-inspired firing-rate computational model involving the hippocampus and surrounding areas, that encompasses two key mechanisms possibly underlying this capability. The first is based on signals encoded by the neuromodulator dopamine, which is released by novel stimuli and enhances plasticity only when needed. The second is based on a homeostatic plasticity mechanism that involves the lateral inhibitory connections of the pyramidal neurons of the hippocampus. These mechanisms tend to protect neurons that have already been heavily employed in encoding previous experiences. The model was tested with images from the MNIST machine learning dataset, and with more naturalistic images, for its ability to mitigate catastrophic interference in lifelong learning. The results show that the proposed biologically grounded mechanisms can effectively enhance the learning of new stimuli while protecting previously acquired knowledge. The proposed mechanisms could be investigated in future empirical animal experiments and inspire machine learning models.
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Affiliation(s)
- Pierangelo Afferni
- Department of Engineering, Campus Bio-Medico University, Rome, Italy
- *Correspondence: Pierangelo Afferni
| | - Federico Cascino-Milani
- Department of Genetics and Neurobiology, Julius-Maximilians Universität Würzburg, Würzburg, Germany
| | - Andrea Mattera
- Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Gianluca Baldassarre
- Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
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8
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Developmental depression-to-facilitation shift controls excitation-inhibition balance. Commun Biol 2022; 5:873. [PMID: 36008708 PMCID: PMC9411206 DOI: 10.1038/s42003-022-03801-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/04/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modeling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of short-lasting memory traces governed by short-term plasticity development. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences. Using computational modelling this study proposes that the commonly observed depression-to-facilitation shift across development controls excitation-inhibition balance in the brain.
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9
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Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks. J Comput Neurosci 2022; 50:357-373. [PMID: 35657570 DOI: 10.1007/s10827-022-00820-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/18/2022] [Accepted: 05/10/2022] [Indexed: 10/18/2022]
Abstract
The brain is believed to operate in part by making predictions about sensory stimuli and encoding deviations from these predictions in the activity of "prediction error neurons." This principle defines the widely influential theory of predictive coding. The precise circuitry and plasticity mechanisms through which animals learn to compute and update their predictions are unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism for training neuronal networks to perform predictive coding. Homeostatic plasticity causes neurons to maintain a steady, baseline firing rate in response to inputs that closely match the inputs on which a network was trained, but firing rates can deviate away from this baseline in response to stimuli that are mismatched from training. We combine computer simulations and mathematical analysis systematically to test the extent to which randomly connected, unstructured networks compute prediction errors after training with homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity alone is sufficient for computing prediction errors for trivial time-constant stimuli, but not for more realistic time-varying stimuli. We use a mean-field theory of plastic networks to explain our findings and characterize the assumptions under which they apply.
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10
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Adaptive erasure of spurious sequences in sensory cortical circuits. Neuron 2022; 110:1857-1868.e5. [PMID: 35358415 PMCID: PMC9616807 DOI: 10.1016/j.neuron.2022.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 11/12/2021] [Accepted: 03/07/2022] [Indexed: 12/02/2022]
Abstract
Sequential activity reflecting previously experienced temporal sequences is considered a hallmark of learning across cortical areas. However, it is unknown how cortical circuits avoid the converse problem: producing spurious sequences that are not reflecting sequences in their inputs. We develop methods to quantify and study sequentiality in neural responses. We show that recurrent circuit responses generally include spurious sequences, which are specifically prevented in circuits that obey two widely known features of cortical microcircuit organization: Dale’s law and Hebbian connectivity. In particular, spike-timing-dependent plasticity in excitation-inhibition networks leads to an adaptive erasure of spurious sequences. We tested our theory in multielectrode recordings from the visual cortex of awake ferrets. Although responses to natural stimuli were largely non-sequential, responses to artificial stimuli initially included spurious sequences, which diminished over extended exposure. These results reveal an unexpected role for Hebbian experience-dependent plasticity and Dale’s law in sensory cortical circuits. Recurrent circuits generate spurious sequences without sequential inputs A principled measure of total sequentiality in population responses is developed Theory predicts that Hebbian plasticity should abolish spurious sequences Spurious sequences in the visual cortex diminish with experience
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11
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Brinkman BAW, Yan H, Maffei A, Park IM, Fontanini A, Wang J, La Camera G. Metastable dynamics of neural circuits and networks. APPLIED PHYSICS REVIEWS 2022; 9:011313. [PMID: 35284030 PMCID: PMC8900181 DOI: 10.1063/5.0062603] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 01/31/2022] [Indexed: 05/14/2023]
Abstract
Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.
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Affiliation(s)
| | - H. Yan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, People's Republic of China
| | | | | | | | - J. Wang
- Authors to whom correspondence should be addressed: and
| | - G. La Camera
- Authors to whom correspondence should be addressed: and
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12
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Gallinaro JV, Clopath C. Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity. PLoS Comput Biol 2021; 17:e1009593. [PMID: 34762644 PMCID: PMC8610285 DOI: 10.1371/journal.pcbi.1009593] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/23/2021] [Accepted: 10/26/2021] [Indexed: 11/19/2022] Open
Abstract
Cell assemblies are thought to be the substrate of memory in the brain. Theoretical studies have previously shown that assemblies can be formed in networks with multiple types of plasticity. But how exactly they are formed and how they encode information is yet to be fully understood. One possibility is that memories are stored in silent assemblies. Here we used a computational model to study the formation of silent assemblies in a network of spiking neurons with excitatory and inhibitory plasticity. We found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals. We also found that this spiking irregularity could be read out with support of short-term plasticity, and that it could contribute to the longevity of memories.
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Affiliation(s)
- Júlia V. Gallinaro
- Bioengineering Department, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Bioengineering Department, Imperial College London, London, United Kingdom
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13
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Lu H, Gallinaro JV, Normann C, Rotter S, Yalcin I. Time Course of Homeostatic Structural Plasticity in Response to Optogenetic Stimulation in Mouse Anterior Cingulate Cortex. Cereb Cortex 2021; 32:1574-1592. [PMID: 34607362 DOI: 10.1093/cercor/bhab281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 11/13/2022] Open
Abstract
Plasticity is the mechanistic basis of development, aging, learning, and memory, both in healthy and pathological brains. Structural plasticity is rarely accounted for in computational network models due to a lack of insight into the underlying neuronal mechanisms and processes. Little is known about how the rewiring of networks is dynamically regulated. To inform such models, we characterized the time course of neural activity, the expression of synaptic proteins, and neural morphology employing an in vivo optogenetic mouse model. We stimulated pyramidal neurons in the anterior cingulate cortex of mice and harvested their brains at 1.5 h, 24 h, and $48\,\mathrm{h}$ after stimulation. Stimulus-induced cortical hyperactivity persisted up to 1.5 h and decayed to baseline after $24\,\mathrm{h}$ indicated by c-Fos expression. The synaptic proteins VGLUT1 and PSD-95, in contrast, were upregulated at $24\,\mathrm{h}$ and downregulated at $48\,\mathrm{h}$, respectively. Spine density and spine head volume were also increased at $24\,\mathrm{h}$ and decreased at $48\,\mathrm{h}$. This specific sequence of events reflects a continuous joint evolution of activity and connectivity that is characteristic of the model of homeostatic structural plasticity. Our computer simulations thus corroborate the observed empirical evidence from our animal experiments.
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Affiliation(s)
- Han Lu
- Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Freiburg 79104, Germany.,Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR3212, Strasbourg 67000, France.,Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg 79104, Germany
| | - Júlia V Gallinaro
- Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Freiburg 79104, Germany.,Bioengineering Department, Imperial College London, London SW7 2AZ, United Kingdom
| | - Claus Normann
- Department of Psychiatry and Psychotherapy, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg 79104, Germany.,Center for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg 79104, Germany
| | - Stefan Rotter
- Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Freiburg 79104, Germany
| | - Ipek Yalcin
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR3212, Strasbourg 67000, France.,Department of Psychiatry and Neuroscience, Université Laval, Québec QC G1V 0A6, Canada
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14
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Manos T, Diaz-Pier S, Tass PA. Long-Term Desynchronization by Coordinated Reset Stimulation in a Neural Network Model With Synaptic and Structural Plasticity. Front Physiol 2021; 12:716556. [PMID: 34566681 PMCID: PMC8455881 DOI: 10.3389/fphys.2021.716556] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/16/2021] [Indexed: 11/16/2022] Open
Abstract
Several brain disorders are characterized by abnormal neuronal synchronization. To specifically counteract abnormal neuronal synchrony and, hence, related symptoms, coordinated reset (CR) stimulation was computationally developed. In principle, successive epochs of synchronizing and desynchronizing stimulation may reversibly move neural networks with plastic synapses back and forth between stable regimes with synchronized and desynchronized firing. Computationally derived predictions have been verified in pre-clinical and clinical studies, paving the way for novel therapies. However, as yet, computational models were not able to reproduce the clinically observed increase of desynchronizing effects of regularly administered CR stimulation intermingled by long stimulation-free epochs. We show that this clinically important phenomenon can be computationally reproduced by taking into account structural plasticity (SP), a mechanism that deletes or generates synapses in order to homeostatically adapt the firing rates of neurons to a set point-like target firing rate in the course of days to months. If we assume that CR stimulation favorably reduces the target firing rate of SP, the desynchronizing effects of CR stimulation increase after long stimulation-free epochs, in accordance with clinically observed phenomena. Our study highlights the pivotal role of stimulation- and dosing-induced modulation of homeostatic set points in therapeutic processes.
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Affiliation(s)
- Thanos Manos
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Laboratoire de Physique Théorique et Modélisation, CNRS, UMR 8089, CY Cergy Paris Université, Cergy-Pontoise Cedex, France
| | - Sandra Diaz-Pier
- Simulation & Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany
| | - Peter A Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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15
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Crodelle J, McLaughlin DW. Modeling the role of gap junctions between excitatory neurons in the developing visual cortex. PLoS Comput Biol 2021; 17:e1007915. [PMID: 34228707 PMCID: PMC8284639 DOI: 10.1371/journal.pcbi.1007915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/16/2021] [Accepted: 06/16/2021] [Indexed: 11/23/2022] Open
Abstract
Recent experiments in the developing mammalian visual cortex have revealed that gap junctions couple excitatory cells and potentially influence the formation of chemical synapses. In particular, cells that were coupled by a gap junction during development tend to share an orientation preference and are preferentially coupled by a chemical synapse in the adult cortex, a property that is diminished when gap junctions are blocked. In this work, we construct a simplified model of the developing mouse visual cortex including spike-timing-dependent plasticity of both the feedforward synaptic inputs and recurrent cortical synapses. We use this model to show that synchrony among gap-junction-coupled cells underlies their preference to form strong recurrent synapses and develop similar orientation preference; this effect decreases with an increase in coupling density. Additionally, we demonstrate that gap-junction coupling works, together with the relative timing of synaptic development of the feedforward and recurrent synapses, to determine the resulting cortical map of orientation preference. Gap junctions, or sites of direct electrical connections between neurons, have a significant presence in the cortex, both during development and in adulthood. Their primary function during either of these periods, however, is still poorly understood. In the adult cortex, gap junctions between local, inhibitory neurons have been shown to promote synchronous firing, a network characteristic thought to be important for learning, attention, and memory. During development, gap junctions between excitatory, pyramidal cells, have been conjectured to play a role in synaptic plasticity and the formation of cortical circuits. In the visual cortex, where neurons exhibit tuned responses to properties of visual input such as orientation and direction, recent experiments show that excitatory cells are coupled by gap junctions during the first postnatal week and are replaced by chemical synapses during the second week. In this work, we explore the possible contribution of gap-junction coupling during development to the formation of chemical synapses between the visual cortex from the thalamus and between cortical cells within the visual cortex. Specifically, using a mathematical model of the visual cortex during development, we identify the response properties of gap-junction-coupled cells and their influence on the formation of the cortical map of orientation preference.
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Affiliation(s)
- Jennifer Crodelle
- Middlebury College, Middlebury, Vermont, United States of America
- Courant Institute of Mathematical Sciences, NYU, New York, New York, United States of America
- * E-mail:
| | - David W. McLaughlin
- Courant Institute of Mathematical Sciences, NYU, New York, New York, United States of America
- Center for Neural Science, NYU, New York, New York, United States of America
- Neuroscience Institute of NYU Langone Health, New York, New York, United States of America
- New York University Shanghai, Shanghai, China
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16
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Akil AE, Rosenbaum R, Josić K. Balanced networks under spike-time dependent plasticity. PLoS Comput Biol 2021; 17:e1008958. [PMID: 33979336 PMCID: PMC8143429 DOI: 10.1371/journal.pcbi.1008958] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 05/24/2021] [Accepted: 04/12/2021] [Indexed: 11/28/2022] Open
Abstract
The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input. Animals are able to learn complex tasks through changes in individual synapses between cells. Such changes lead to the coevolution of neural activity patterns and the structure of neural connectivity, but the consequences of these interactions are not fully understood. We consider plasticity in model neural networks which achieve an average balance between the excitatory and inhibitory synaptic inputs to different cells, and display cortical–like, irregular activity. We extend the theory of balanced networks to account for synaptic plasticity and show which rules can maintain balance, and which will drive the network into a different state. This theory of plasticity can provide insights into the relationship between stimuli, network dynamics, and synaptic circuitry.
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Affiliation(s)
- Alan Eric Akil
- Department of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, United States of America
- Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas, United States of America
- Department of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America
- * E-mail:
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17
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Mackwood O, Naumann LB, Sprekeler H. Learning excitatory-inhibitory neuronal assemblies in recurrent networks. eLife 2021; 10:59715. [PMID: 33900199 PMCID: PMC8075581 DOI: 10.7554/elife.59715] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 03/03/2021] [Indexed: 12/22/2022] Open
Abstract
Understanding the connectivity observed in the brain and how it emerges from local plasticity rules is a grand challenge in modern neuroscience. In the primary visual cortex (V1) of mice, synapses between excitatory pyramidal neurons and inhibitory parvalbumin-expressing (PV) interneurons tend to be stronger for neurons that respond to similar stimulus features, although these neurons are not topographically arranged according to their stimulus preference. The presence of such excitatory-inhibitory (E/I) neuronal assemblies indicates a stimulus-specific form of feedback inhibition. Here, we show that activity-dependent synaptic plasticity on input and output synapses of PV interneurons generates a circuit structure that is consistent with mouse V1. Computational modeling reveals that both forms of plasticity must act in synergy to form the observed E/I assemblies. Once established, these assemblies produce a stimulus-specific competition between pyramidal neurons. Our model suggests that activity-dependent plasticity can refine inhibitory circuits to actively shape cortical computations.
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Affiliation(s)
- Owen Mackwood
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
| | - Laura B Naumann
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
| | - Henning Sprekeler
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
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18
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Generation of Sharp Wave-Ripple Events by Disinhibition. J Neurosci 2020; 40:7811-7836. [PMID: 32913107 PMCID: PMC7548694 DOI: 10.1523/jneurosci.2174-19.2020] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 06/29/2020] [Accepted: 07/17/2020] [Indexed: 11/21/2022] Open
Abstract
Sharp wave-ripple complexes (SWRs) are hippocampal network phenomena involved in memory consolidation. To date, the mechanisms underlying their occurrence remain obscure. Here, we show how the interactions between pyramidal cells, parvalbumin-positive (PV+) basket cells, and an unidentified class of anti-SWR interneurons can contribute to the initiation and termination of SWRs. Using a biophysically constrained model of a network of spiking neurons and a rate-model approximation, we demonstrate that SWRs emerge as a result of the competition between two interneuron populations and the resulting disinhibition of pyramidal cells. Our models explain how the activation of pyramidal cells or PV+ cells can trigger SWRs, as shown in vitro, and suggests that PV+ cell-mediated short-term synaptic depression influences the experimentally reported dynamics of SWR events. Furthermore, we predict that the silencing of anti-SWR interneurons can trigger SWRs. These results broaden our understanding of the microcircuits supporting the generation of memory-related network dynamics. SIGNIFICANCE STATEMENT The hippocampus is a part of the mammalian brain that is crucial for episodic memories. During periods of sleep and inactive waking, the extracellular activity of the hippocampus is dominated by sharp wave-ripple events (SWRs), which have been shown to be important for memory consolidation. The mechanisms regulating the emergence of these events are still unclear. We developed a computational model to study the emergence of SWRs and to explain the roles of different cell types in regulating them. The model accounts for several previously unexplained features of SWRs and thus advances the understanding of memory-related dynamics.
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19
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Herstel LJ, Wierenga CJ. Network control through coordinated inhibition. Curr Opin Neurobiol 2020; 67:34-41. [PMID: 32853970 DOI: 10.1016/j.conb.2020.08.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 07/29/2020] [Accepted: 08/01/2020] [Indexed: 12/29/2022]
Abstract
Coordinated excitatory and inhibitory activity is required for proper brain functioning. Recent computational and experimental studies have demonstrated that activity patterns in recurrent cortical networks are dominated by inhibition. Whereas previous studies have suggested that inhibitory plasticity is important for homeostatic control, this new framework puts inhibition in the driver's seat. Complex neuronal networks in the brain comprise many configurations in parallel, controlled by external and internal 'switches'. Context-dependent modulation and plasticity of inhibitory connections play a key role in memory and learning. It is therefore important to realize that synaptic plasticity is often multisynaptic and that a proper balance between excitation and inhibition is not fixed, but depends on context and activity level.
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Affiliation(s)
- Lotte J Herstel
- Cell Biology, Neurobiology and Biophysics, Biology Department, Faculty of Science, Utrecht University, The Netherlands
| | - Corette J Wierenga
- Cell Biology, Neurobiology and Biophysics, Biology Department, Faculty of Science, Utrecht University, The Netherlands.
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20
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Kozachkov L, Lundqvist M, Slotine JJ, Miller EK. Achieving stable dynamics in neural circuits. PLoS Comput Biol 2020; 16:e1007659. [PMID: 32764745 PMCID: PMC7446801 DOI: 10.1371/journal.pcbi.1007659] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 08/19/2020] [Accepted: 06/27/2020] [Indexed: 01/01/2023] Open
Abstract
The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying.
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Affiliation(s)
- Leo Kozachkov
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Nonlinear Systems Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
| | - Mikael Lundqvist
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Jean-Jacques Slotine
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Nonlinear Systems Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
| | - Earl K. Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
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21
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Capogna M, Castillo PE, Maffei A. The ins and outs of inhibitory synaptic plasticity: Neuron types, molecular mechanisms and functional roles. Eur J Neurosci 2020; 54:6882-6901. [PMID: 32663353 DOI: 10.1111/ejn.14907] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 06/30/2020] [Accepted: 07/08/2020] [Indexed: 01/05/2023]
Abstract
GABAergic interneurons are highly diverse, and their synaptic outputs express various forms of plasticity. Compelling evidence indicates that activity-dependent changes of inhibitory synaptic transmission play a significant role in regulating neural circuits critically involved in learning and memory and circuit refinement. Here, we provide an updated overview of inhibitory synaptic plasticity with a focus on the hippocampus and neocortex. To illustrate the diversity of inhibitory interneurons, we discuss the case of two highly divergent interneuron types, parvalbumin-expressing basket cells and neurogliaform cells, which support unique roles on circuit dynamics. We also present recent progress on the molecular mechanisms underlying long-term, activity-dependent plasticity of fast inhibitory transmission. Lastly, we discuss the role of inhibitory synaptic plasticity in neuronal circuits' function. The emerging picture is that inhibitory synaptic transmission in the CNS is extremely diverse, undergoes various mechanistically distinct forms of plasticity and contributes to a much more refined computational role than initially thought. Both the remarkable diversity of inhibitory interneurons and the various forms of plasticity expressed by GABAergic synapses provide an amazingly rich inhibitory repertoire that is central to a variety of complex neural circuit functions, including memory.
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Affiliation(s)
- Marco Capogna
- Department of Biomedicine, Danish National Research Foundation Center of Excellence PROMEMO, Aarhus University, Aarhus, Denmark
| | - Pablo E Castillo
- Dominck P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Arianna Maffei
- Center for Neural Circuit Dynamics and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, USA
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22
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Kim SY, Lim W. Effect of interpopulation spike-timing-dependent plasticity on synchronized rhythms in neuronal networks with inhibitory and excitatory populations. Cogn Neurodyn 2020; 14:535-567. [PMID: 32655716 DOI: 10.1007/s11571-020-09580-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/11/2020] [Accepted: 03/06/2020] [Indexed: 02/07/2023] Open
Abstract
We consider a two-population network consisting of both inhibitory (I) interneurons and excitatory (E) pyramidal cells. This I-E neuronal network has adaptive dynamic I to E and E to I interpopulation synaptic strengths, governed by interpopulation spike-timing-dependent plasticity (STDP). In previous works without STDPs, fast sparsely synchronized rhythms, related to diverse cognitive functions, were found to appear in a range of noise intensity D for static synaptic strengths. Here, by varying D, we investigate the effect of interpopulation STDPs on fast sparsely synchronized rhythms that emerge in both the I- and the E-populations. Depending on values of D, long-term potentiation (LTP) and long-term depression (LTD) for population-averaged values of saturated interpopulation synaptic strengths are found to occur. Then, the degree of fast sparse synchronization varies due to effects of LTP and LTD. In a broad region of intermediate D, the degree of good synchronization (with higher synchronization degree) becomes decreased, while in a region of large D, the degree of bad synchronization (with lower synchronization degree) gets increased. Consequently, in each I- or E-population, the synchronization degree becomes nearly the same in a wide range of D (including both the intermediate and the large D regions). This kind of "equalization effect" is found to occur via cooperative interplay between the average occupation and pacing degrees of spikes (i.e., the average fraction of firing neurons and the average degree of phase coherence between spikes in each synchronized stripe of spikes in the raster plot of spikes) in fast sparsely synchronized rhythms. Finally, emergences of LTP and LTD of interpopulation synaptic strengths (leading to occurrence of equalization effect) are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times.
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Affiliation(s)
- Sang-Yoon Kim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu, 42411 Korea
| | - Woochang Lim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu, 42411 Korea
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23
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Gandolfi D, Bigiani A, Porro CA, Mapelli J. Inhibitory Plasticity: From Molecules to Computation and Beyond. Int J Mol Sci 2020; 21:ijms21051805. [PMID: 32155701 PMCID: PMC7084224 DOI: 10.3390/ijms21051805] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/28/2020] [Accepted: 03/03/2020] [Indexed: 11/17/2022] Open
Abstract
Synaptic plasticity is the cellular and molecular counterpart of learning and memory and, since its first discovery, the analysis of the mechanisms underlying long-term changes of synaptic strength has been almost exclusively focused on excitatory connections. Conversely, inhibition was considered as a fixed controller of circuit excitability. Only recently, inhibitory networks were shown to be finely regulated by a wide number of mechanisms residing in their synaptic connections. Here, we review recent findings on the forms of inhibitory plasticity (IP) that have been discovered and characterized in different brain areas. In particular, we focus our attention on the molecular pathways involved in the induction and expression mechanisms leading to changes in synaptic efficacy, and we discuss, from the computational perspective, how IP can contribute to the emergence of functional properties of brain circuits.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
- Department of Brain and behavioral sciences, University of Pavia, 27100 Pavia, Italy
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
| | - Carlo Adolfo Porro
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
- Correspondence: ; Tel.: +39-059-205- 5459
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24
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Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients. PLoS One 2020; 15:e0221380. [PMID: 32027654 PMCID: PMC7004372 DOI: 10.1371/journal.pone.0221380] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 01/18/2020] [Indexed: 11/25/2022] Open
Abstract
Computational studies of the influence of different network parameters on the dynamic and topological network effects of brain stimulation can enhance our understanding of different outcomes between individuals. In this study, a brain stimulation session along with the subsequent post-stimulation brain activity is simulated for a period of one day using a network of modified Wilson-Cowan oscillators coupled according to diffusion imaging based structural connectivity. We use this computational model to examine how differences in the inter-region connectivity and the excitability of stimulated regions at the time of stimulation can affect post-stimulation behaviours. Our findings indicate that the initial inter-region connectivity can heavily affect the changes that stimulation induces in the connectivity of the network. Moreover, differences in the excitability of the stimulated regions seem to lead to different post-stimulation connectivity changes across the model network, including on the internal connectivity of non-stimulated regions.
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25
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Vilimelis Aceituno P, Ehsani M, Jost J. Spiking time-dependent plasticity leads to efficient coding of predictions. BIOLOGICAL CYBERNETICS 2020; 114:43-61. [PMID: 31873797 PMCID: PMC7062862 DOI: 10.1007/s00422-019-00813-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 12/13/2019] [Indexed: 06/10/2023]
Abstract
Latency reduction in postsynaptic spikes is a well-known effect of spiking time-dependent plasticity. We expand this notion for long postsynaptic spike trains on single neurons, showing that, for a fixed input spike train, STDP reduces the number of postsynaptic spikes and concentrates the remaining ones. Then, we study the consequences of this phenomena in terms of coding, finding that this mechanism improves the neural code by increasing the signal-to-noise ratio and lowering the metabolic costs of frequent stimuli. Finally, we illustrate that the reduction in postsynaptic latencies can lead to the emergence of predictions.
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Affiliation(s)
- Pau Vilimelis Aceituno
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany.
- Max Planck School of Cognition, Stephanstraße 1a, 04103, Leipzig, Germany.
| | - Masud Ehsani
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA
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26
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Inhibitory microcircuits for top-down plasticity of sensory representations. Nat Commun 2019; 10:5055. [PMID: 31699994 PMCID: PMC6838080 DOI: 10.1038/s41467-019-12972-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 10/11/2019] [Indexed: 01/06/2023] Open
Abstract
Rewards influence plasticity of early sensory representations, but the underlying changes in circuitry are unclear. Recent experimental findings suggest that inhibitory circuits regulate learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards interacts with local plasticity to induce long-lasting changes in circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can store information about rewarded stimuli to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons develop strong connections to parvalbumin-positive interneurons during reward such that they selectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leading to increased stimulus representation. We make specific testable predictions and show that this two-stage model allows for translation invariance of the learned representation. Rewards can improve stimulus processing in early sensory areas but the underlying neural circuit mechanisms are unknown. Here, the authors build a computational model of layer 2/3 primary visual cortex and suggest that plastic inhibitory circuits change first and then increase excitatory representations beyond the presence of rewards.
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27
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Murray LM, Knikou M. Repeated cathodal transspinal pulse and direct current stimulation modulate cortical and corticospinal excitability differently in healthy humans. Exp Brain Res 2019; 237:1841-1852. [DOI: 10.1007/s00221-019-05559-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 05/08/2019] [Indexed: 10/26/2022]
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28
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Valtcheva S, Venance L. Control of Long-Term Plasticity by Glutamate Transporters. Front Synaptic Neurosci 2019; 11:10. [PMID: 31024287 PMCID: PMC6465798 DOI: 10.3389/fnsyn.2019.00010] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 03/12/2019] [Indexed: 12/11/2022] Open
Abstract
Activity-dependent long-term changes in synaptic strength constitute key elements for learning and memory formation. Long-term plasticity can be induced in vivo and ex vivo by various physiologically relevant activity patterns. Depending on their temporal statistics, such patterns can induce long-lasting changes in the synaptic weight by potentiating or depressing synaptic transmission. At excitatory synapses, glutamate uptake operated by excitatory amino acid transporters (EAATs) has a critical role in regulating the strength and the extent of receptor activation by afferent activity. EAATs tightly control synaptic transmission and glutamate spillover. EAATs activity can, therefore, determine the polarity and magnitude of long-term plasticity by regulating the spatiotemporal profile of the glutamate transients and thus, the glutamate access to pre- and postsynaptic receptors. Here, we summarize compelling evidence that EAATs regulate various forms of long-term synaptic plasticity and the consequences of such regulation for behavioral output. We speculate that experience-dependent plasticity of EAATs levels can determine the sensitivity of synapses to frequency- or time-dependent plasticity paradigms. We propose that EAATs contribute to the gating of relevant inputs eligible to induce long-term plasticity and thereby select the operating learning rules that match the physiological function of the synapse adapted to the behavioral context.
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Affiliation(s)
- Silvana Valtcheva
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS UMR7241/INSERM U1050, Paris, France
| | - Laurent Venance
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS UMR7241/INSERM U1050, Paris, France
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29
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Haga T, Fukai T. Dendritic processing of spontaneous neuronal sequences for single-trial learning. Sci Rep 2018; 8:15166. [PMID: 30310112 PMCID: PMC6181986 DOI: 10.1038/s41598-018-33513-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 10/01/2018] [Indexed: 11/29/2022] Open
Abstract
Spontaneous firing sequences are ubiquitous in cortical networks, but their roles in cellular and network-level computations remain unexplored. In the hippocampus, such sequences, conventionally called preplay, have been hypothesized to participate in learning and memory. Here, we present a computational model for encoding input sequence patterns into internal network states based on the propagation of preplay sequences in recurrent neuronal networks. The model instantiates two synaptic pathways in cortical neurons, one for proximal dendrite-somatic interactions to generate intrinsic preplay sequences and the other for distal dendritic processing of extrinsic signals. The core dendritic computation is the maximization of matching between patterned activities in the two compartments through nonlinear spike generation. The model performs robust single-trial learning with long-term stability and independence that are modulated by the plasticity of dendrite-targeted inhibition. Our results demonstrate that dendritic computation enables somatic spontaneous firing sequences to act as templates for rapid and stable memory formation.
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Affiliation(s)
- Tatsuya Haga
- RIKEN Center for Brain Science, Hirosawa 2-1, Wako, Saitama, 351-0198, Japan.
| | - Tomoki Fukai
- RIKEN Center for Brain Science, Hirosawa 2-1, Wako, Saitama, 351-0198, Japan.
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30
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Cabessa J, Villa AEP. Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters. CHAOS (WOODBURY, N.Y.) 2018; 28:106318. [PMID: 30384642 DOI: 10.1063/1.5042312] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 08/29/2018] [Indexed: 06/08/2023]
Abstract
Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.
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Affiliation(s)
- Jérémie Cabessa
- Laboratory of Mathematical Economics (LEMMA), Université Paris 2-Panthéon-Assas, 75005 Paris, France
| | - Alessandro E P Villa
- Neuroheuristic Research Group, University of Lausanne, CH-1015 Lausanne, Switzerland
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Kim SY, Lim W. Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity. Cogn Neurodyn 2018; 13:53-73. [PMID: 30728871 DOI: 10.1007/s11571-018-9505-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 08/19/2018] [Accepted: 08/28/2018] [Indexed: 01/09/2023] Open
Abstract
We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabási-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l ∗ and the asymmetry parameter Δ l in the SFN.
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Affiliation(s)
- Sang-Yoon Kim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu, 42411 Korea
| | - Woochang Lim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu, 42411 Korea
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Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses. Sci Rep 2018; 8:13050. [PMID: 30158555 PMCID: PMC6115462 DOI: 10.1038/s41598-018-31412-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 08/07/2018] [Indexed: 11/08/2022] Open
Abstract
Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity have been associated with pathological states. However, the mechanism underlying these rhythms remains unknown. Here, we present a theoretical analysis of the evolvement of rhythm generating capabilities in neuronal circuits. We tested the hypothesis that brain rhythms can be acquired via an intrinsic unsupervised learning process of activity dependent plasticity. Specifically, we focused on spike timing dependent plasticity (STDP) of inhibitory synapses. We detail how rhythmicity can develop via STDP under certain conditions that serve as a natural prediction of the hypothesis. We show how global features of the STDP rule govern and stabilize the resultant rhythmic activity. Finally, we demonstrate how rhythmicity is retained even in the face of synaptic variability. This study suggests a role for inhibitory plasticity that is beyond homeostatic processes.
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Kim SY, Lim W. Effect of inhibitory spike-timing-dependent plasticity on fast sparsely synchronized rhythms in a small-world neuronal network. Neural Netw 2018; 106:50-66. [PMID: 30025272 DOI: 10.1016/j.neunet.2018.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 05/14/2018] [Accepted: 06/25/2018] [Indexed: 02/06/2023]
Abstract
We consider the Watts-Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity D. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of D, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of D. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good synchronization (with higher spiking measure) is found to get better via LTD, while bad synchronization (with lower spiking measure) is found to get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability p of the SWN in the presence of iSTDP.
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Affiliation(s)
- Sang-Yoon Kim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 42411, Republic of Korea.
| | - Woochang Lim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 42411, Republic of Korea.
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Bayat Mokhtari E, Lawrence JJ, Stone EF. Effect of Neuromodulation of Short-term Plasticity on Information Processing in Hippocampal Interneuron Synapses. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2018; 8:7. [PMID: 29845383 PMCID: PMC5975118 DOI: 10.1186/s13408-018-0062-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 05/17/2018] [Indexed: 06/08/2023]
Abstract
Neurons in a micro-circuit connected by chemical synapses can have their connectivity affected by the prior activity of the cells. The number of synapses available for releasing neurotransmitter can be decreased by repetitive activation through depletion of readily releasable neurotransmitter (NT), or increased through facilitation, where the probability of release of NT is increased by prior activation. These competing effects can create a complicated and subtle range of time-dependent connectivity. Here we investigate the probabilistic properties of facilitation and depression (FD) for a presynaptic neuron that is receiving a Poisson spike train of input. We use a model of FD that is parameterized with experimental data from a hippocampal basket cell and pyramidal cell connection, for fixed frequency input spikes at frequencies in the range of theta (3-8 Hz) and gamma (20-100 Hz) oscillations. Hence our results will apply to micro-circuits in the hippocampus that are responsible for the interaction of theta and gamma rhythms associated with learning and memory. A control situation is compared with one in which a pharmaceutical neuromodulator (muscarine) is employed. We apply standard information-theoretic measures such as entropy and mutual information, and find a closed form approximate expression for the probability distribution of release probability. We also use techniques that measure the dependence of the response on the exact history of stimulation the synapse has received, which uncovers some unexpected differences between control and muscarine-added cases.
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Affiliation(s)
| | - J. Josh Lawrence
- Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, USA
| | - Emily F. Stone
- Department of Mathematical Sciences, The University of Montana, Missoula, USA
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Winters BD, Golding NL. Glycinergic Inhibitory Plasticity in Binaural Neurons Is Cumulative and Gated by Developmental Changes in Action Potential Backpropagation. Neuron 2018; 98:166-178.e2. [PMID: 29576388 PMCID: PMC5886803 DOI: 10.1016/j.neuron.2018.03.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 01/09/2018] [Accepted: 02/28/2018] [Indexed: 11/20/2022]
Abstract
Utilization of timing-based sound localization cues by neurons in the medial superior olive (MSO) depends critically on glycinergic inhibitory inputs. After hearing onset, the strength and subcellular location of these inhibitory inputs are dramatically altered, but the cellular processes underlying this experience-dependent refinement are unknown. Here we reveal a form of inhibitory long-term potentiation (iLTP) in MSO neurons that is dependent on spiking and synaptic activation but is not affected by their fine-scale relative timing at higher frequencies prevalent in auditory circuits. We find that iLTP reinforces inhibitory inputs coactive with binaural excitation in a cumulative manner, likely well suited for networks featuring persistent high-frequency activity. We also show that a steep drop in action potential size and backpropagation limits induction of iLTP to the first 2 weeks of hearing. These intrinsic changes would deprive more distal inhibitory synapses of reinforcement, conceivably establishing the mature, soma-biased pattern of inhibition.
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Affiliation(s)
- Bradley D Winters
- The University of Texas at Austin, Department of Neuroscience and Center for Learning and Memory, 1 University Station C7000, Austin TX 78712-0248, USA
| | - Nace L Golding
- The University of Texas at Austin, Department of Neuroscience and Center for Learning and Memory, 1 University Station C7000, Austin TX 78712-0248, USA.
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36
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Gambrill AC, Faulkner RL, Cline HT. Direct intertectal inputs are an integral component of the bilateral sensorimotor circuit for behavior in Xenopus tadpoles. J Neurophysiol 2018; 119:1947-1961. [PMID: 29442555 DOI: 10.1152/jn.00051.2018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The circuit controlling visually guided behavior in nonmammalian vertebrates, such as Xenopus tadpoles, includes retinal projections to the contralateral optic tectum, where visual information is processed, and tectal motor outputs projecting ipsilaterally to hindbrain and spinal cord. Tadpoles have an intertectal commissure whose function is unknown, but it might transfer information between the tectal lobes. Differences in visual experience between the two eyes have profound effects on the development and function of visual circuits in animals with binocular vision, but the effects on animals with fully crossed retinal projections are not clear. We tested the effect of monocular visual experience on the visuomotor circuit in Xenopus tadpoles. We show that cutting the intertectal commissure or providing visual experience to one eye (monocular visual experience) is sufficient to disrupt tectally mediated visual avoidance behavior. Monocular visual experience induces asymmetry in tectal circuit activity across the midline. Repeated exposure to monocular visual experience drives maturation of the stimulated retinotectal synapses, seen as increased AMPA-to-NMDA ratios, induces synaptic plasticity in intertectal synaptic connections, and induces bilaterally asymmetric changes in the tectal excitation-to-inhibition ratio (E/I). We show that unilateral expression of peptides that interfere with AMPA or GABAA receptor trafficking alters E/I in the transfected tectum and is sufficient to degrade visuomotor behavior. Our study demonstrates that monocular visual experience in animals with fully crossed visual systems produces asymmetric circuit function across the midline and degrades visuomotor behavior. The data further suggest that intertectal inputs are an integral component of a bilateral visuomotor circuit critical for behavior. NEW & NOTEWORTHY The developing optic tectum of Xenopus tadpoles represents a unique circuit in which laterally positioned eyes provide sensory input to a circuit that is transiently monocular, but which will be binocular in the animal's adulthood. We challenge the idea that the two lobes of tadpole optic tectum function independently by testing the requirement of interhemispheric communication and demonstrate that unbalanced sensory input can induce structural and functional plasticity in the tectum sufficient to disrupt function.
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Affiliation(s)
- Abigail C Gambrill
- Department of Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute , La Jolla, California
| | - Regina L Faulkner
- Department of Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute , La Jolla, California
| | - Hollis T Cline
- Department of Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute , La Jolla, California
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37
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Abeysuriya RG, Hadida J, Sotiropoulos SN, Jbabdi S, Becker R, Hunt BAE, Brookes MJ, Woolrich MW. A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. PLoS Comput Biol 2018; 14:e1006007. [PMID: 29474352 PMCID: PMC5841816 DOI: 10.1371/journal.pcbi.1006007] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 03/07/2018] [Accepted: 01/28/2018] [Indexed: 01/03/2023] Open
Abstract
Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.
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Affiliation(s)
- Romesh G. Abeysuriya
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
| | - Jonathan Hadida
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham
| | - Saad Jbabdi
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Robert Becker
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
| | - Benjamin A. E. Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
- Department of Diagnostic Imaging, Neurosciences & Mental Health, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
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38
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Effect of spike-timing-dependent plasticity on stochastic burst synchronization in a scale-free neuronal network. Cogn Neurodyn 2018; 12:315-342. [PMID: 29765480 DOI: 10.1007/s11571-017-9470-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 11/29/2017] [Accepted: 12/26/2017] [Indexed: 01/02/2023] Open
Abstract
We consider an excitatory population of subthreshold Izhikevich neurons which cannot fire spontaneously without noise. As the coupling strength passes a threshold, individual neurons exhibit noise-induced burstings. This neuronal population has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). However, STDP was not considered in previous works on stochastic burst synchronization (SBS) between noise-induced burstings of sub-threshold neurons. Here, we study the effect of additive STDP on SBS by varying the noise intensity D in the Barabási-Albert scale-free network (SFN). One of our main findings is a Matthew effect in synaptic plasticity which occurs due to a positive feedback process. Good burst synchronization (with higher bursting measure) gets better via long-term potentiation (LTP) of synaptic strengths, while bad burst synchronization (with lower bursting measure) gets worse via long-term depression (LTD). Consequently, a step-like rapid transition to SBS occurs by changing D, in contrast to a relatively smooth transition in the absence of STDP. We also investigate the effects of network architecture on SBS by varying the symmetric attachment degree [Formula: see text] and the asymmetry parameter [Formula: see text] in the SFN, and Matthew effects are also found to occur by varying [Formula: see text] and [Formula: see text]. Furthermore, emergences of LTP and LTD of synaptic strengths are investigated in details via our own microscopic methods based on both the distributions of time delays between the burst onset times of the pre- and the post-synaptic neurons and the pair-correlations between the pre- and the post-synaptic instantaneous individual burst rates (IIBRs). Finally, a multiplicative STDP case (depending on states) with soft bounds is also investigated in comparison with the additive STDP case (independent of states) with hard bounds. Due to the soft bounds, a Matthew effect with some quantitative differences is also found to occur for the case of multiplicative STDP.
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Xu Z, Skorheim S, Tu M, Berisha V, Yu S, Seo JS, Bazhenov M, Cao Y. Improving efficiency in sparse learning with the feedforward inhibitory motif. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.05.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Zenke F, Gerstner W. Hebbian plasticity requires compensatory processes on multiple timescales. Philos Trans R Soc Lond B Biol Sci 2017; 372:rstb.2016.0259. [PMID: 28093557 PMCID: PMC5247595 DOI: 10.1098/rstb.2016.0259] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2016] [Indexed: 01/19/2023] Open
Abstract
We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.
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Affiliation(s)
- Friedemann Zenke
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Wulfram Gerstner
- Brain Mind Institute, School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne EPFL, Switzerland
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41
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Emerging Mechanisms Underlying Dynamics of GABAergic Synapses. J Neurosci 2017; 37:10792-10799. [PMID: 29118207 DOI: 10.1523/jneurosci.1824-17.2017] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 09/25/2017] [Accepted: 10/11/2017] [Indexed: 11/21/2022] Open
Abstract
Inhibitory circuits are diverse, yet with a poorly understood cell biology. Functional characterization of distinct inhibitory neuron subtypes has not been sufficient to explain how GABAergic neurotransmission sculpts principal cell activity in a relevant fashion. Our Mini-Symposium brings together several emerging mechanisms that modulate GABAergic neurotransmission dynamically from either the presynaptic or the postsynaptic site. The first two talks discuss novel developmental and neuronal subtype-specific contributions to the excitatory/inhibitory balance and circuit maturation. The next three talks examine how interactions between cellular pathways, lateral diffusion of proteins between synapses, and chloride transporter function at excitatory and inhibitory synapses and facilitate inhibitory synapse adaptations. Finally, we address functional differences within GABAergic interneurons to highlight the importance of diverse, flexible, and versatile inputs that shape network function. Together, the selection of topics demonstrates how developmental and activity-dependent mechanisms coordinate inhibition in relation to the excitatory inputs and vice versa.
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42
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Simultaneously Excitatory and Inhibitory Effects of Transcranial Alternating Current Stimulation Revealed Using Selective Pulse-Train Stimulation in the Rat Motor Cortex. J Neurosci 2017; 37:9389-9402. [PMID: 28847809 DOI: 10.1523/jneurosci.1390-17.2017] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 08/03/2017] [Accepted: 08/10/2017] [Indexed: 12/13/2022] Open
Abstract
Transcranial alternating current stimulation (tACS) uses sinusoidal, subthreshold, electric fields to modulate cortical processing. Cortical processing depends on a fine balance between excitation and inhibition and tACS acts on both excitatory and inhibitory cortical neurons. Given this, it is not clear whether tACS should increase or decrease cortical excitability. We investigated this using transcranial current stimulation of the rat (all males) motor cortex consisting of a continuous subthreshold sine wave with short bursts of suprathreshold pulse-trains inserted at different phases to probe cortical excitability. We found that when a low-rate, long-duration, suprathreshold pulse-train was used, subthreshold cathodal tACS decreased cortical excitability and anodal tACS increased excitability. However, when a high-rate, short-duration, suprathreshold pulse-train was used this pattern was inverted. An integrate-and-fire model incorporating biophysical differences between cortical excitatory and inhibitory neurons could predict the experimental data and helped interpret these results. The model indicated that low-rate suprathreshold pulse-trains preferentially stimulate excitatory cortical neurons, whereas high-rate suprathreshold pulse-trains stimulate both excitatory and inhibitory neurons. If correct, this indicates that suprathreshold pulse-train stimulation may be able to selectively control the excitation-inhibition balance within a cortical network. The excitation-inhibition balance then likely plays an important role in determining whether subthreshold tACS will increase or decrease cortical excitability.SIGNIFICANCE STATEMENT Transcranial alternating current stimulation (tACS) is a noninvasive neuromodulation method that uses weak sinusoidal electric fields to modulate cortical activity. In healthy volunteers tACS can modulate perception, cognition, and motor function but the underlying neural mechanism is poorly understood. In this study, using rat motor cortex, we found that tACS effects are highly variable: applying the same tACS waveform to the same cortical area does not always give the same change in cortical excitability. An integrate-and-fire model incorporating excitatory pyramidal and inhibitory interneurons indicated that tACS effects likely depend on the cortical excitation-inhibition balance. When cortical activity is excitation dominated one particular tACS phase increases excitability, but when the cortical activity is inhibition dominated the same tACS phase actually decreases excitability.
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Hennequin G, Agnes EJ, Vogels TP. Inhibitory Plasticity: Balance, Control, and Codependence. Annu Rev Neurosci 2017; 40:557-579. [DOI: 10.1146/annurev-neuro-072116-031005] [Citation(s) in RCA: 140] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Guillaume Hennequin
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
| | - Everton J. Agnes
- Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3SR, United Kingdom
| | - Tim P. Vogels
- Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3SR, United Kingdom
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Abstract
Nervous systems use excitatory cell assemblies to encode and represent sensory percepts. Similarly, synaptically connected cell assemblies or "engrams" are thought to represent memories of past experience. Multiple lines of recent evidence indicate that brain systems create and use inhibitory replicas of excitatory representations for important cognitive functions. Such matched "inhibitory engrams" can form through homeostatic potentiation of inhibition onto postsynaptic cells that show increased levels of excitation. Inhibitory engrams can reduce behavioral responses to familiar stimuli, thereby resulting in behavioral habituation. In addition, by preventing inappropriate activation of excitatory memory engrams, inhibitory engrams can make memories quiescent, stored in a latent form that is available for context-relevant activation. In neural networks with balanced excitatory and inhibitory engrams, the release of innate responses and recall of associative memories can occur through focused disinhibition. Understanding mechanisms that regulate the formation and expression of inhibitory engrams in vivo may help not only to explain key features of cognition but also to provide insight into transdiagnostic traits associated with psychiatric conditions such as autism, schizophrenia, and posttraumatic stress disorder.
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45
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Cho CH. Commentary: GARLH Family Proteins Stabilize GABAA Receptors at Synapses. Front Mol Neurosci 2017; 10:169. [PMID: 28611592 PMCID: PMC5447330 DOI: 10.3389/fnmol.2017.00169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 05/15/2017] [Indexed: 11/13/2022] Open
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46
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Kaczor PT, Mozrzymas JW. Key Metabolic Enzymes Underlying Astrocytic Upregulation of GABAergic Plasticity. Front Cell Neurosci 2017; 11:144. [PMID: 28559800 PMCID: PMC5432623 DOI: 10.3389/fncel.2017.00144] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 05/02/2017] [Indexed: 11/30/2022] Open
Abstract
GABAergic plasticity is recognized as a key mechanism of shaping the activity of the neuronal networks. However, its description is challenging because of numerous neuron-specific mechanisms. In particular, while essential role of glial cells in the excitatory plasticity is well established, their involvement in GABAergic plasticity only starts to emerge. To address this problem, we used two models: neuronal cell culture (NC) and astrocyte-neuronal co-culture (ANCC), where we chemically induced long-term potentiation at inhibitory synapses (iLTP). iLTP could be induced both in NC and ANCC but in ANCC its extent was larger. Importantly, this functional iLTP manifestation was accompanied by an increase in gephyrin puncta size. Furthermore, blocking astrocyte Krebs cycle with fluoroacetate (FA) in ANCC prevented enhancement of both mIPSC amplitude and gephyrin puncta size but this effect was not observed in NC, indicating a key role in neuron-astrocyte cross-talk. Blockade of monocarboxylate transport with α-Cyano-4-hydroxycinnamic acid (4CIN) abolished iLTP both in NC and ANCC and in the latter model prevented also enlargement of gephyrin puncta. Similarly, blockade of glycogen phosphorylase with BAYU6751 prevented enlargement of gephyrin puncta upon iLTP induction. Finally, block of glutamine synthetase with methionine sulfoxide (MSO) nearly abolished mIPSC increase in both NMDA stimulated cell groups but did not prevent enlargement of gephyrin puncta. In conclusion, we provide further evidence that GABAergic plasticity is strongly regulated by astrocytes and the underlying mechanisms involve key metabolic enzymes. Considering the strategic role of GABAergic interneurons, the plasticity described here indicates possible mechanism whereby metabolism regulates the network activity.
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Affiliation(s)
- Przemysław T Kaczor
- Department of Molecular Physiology and Neurobiology, Faculty of Biological Sciences, University of WrocławWrocław, Poland
| | - Jerzy W Mozrzymas
- Department of Molecular Physiology and Neurobiology, Faculty of Biological Sciences, University of WrocławWrocław, Poland.,Laboratory of Neuroscience, Department of Biophysics, Wrocław Medical UniversityWrocław, Poland
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47
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Sprekeler H. Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond. Curr Opin Neurobiol 2017; 43:198-203. [PMID: 28500933 DOI: 10.1016/j.conb.2017.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Revised: 03/12/2017] [Accepted: 03/22/2017] [Indexed: 11/18/2022]
Abstract
Computational neuroscience has a long-standing tradition of investigating the consequences of excitatory synaptic plasticity. In contrast, the functions of inhibitory plasticity are still largely nebulous, particularly given the bewildering diversity of interneurons in the brain. Here, we review recent computational advances that provide first suggestions for the functional roles of inhibitory plasticity, such as a maintenance of the excitation-inhibition balance, a stabilization of recurrent network dynamics and a decorrelation of sensory responses. The field is still in its infancy, but given the existing body of theory for excitatory plasticity, it is likely to mature quickly and deliver important insights into the self-organization of inhibitory circuits in the brain.
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Affiliation(s)
- Henning Sprekeler
- Department for Electrical Engineering and Computer Science, Berlin Institute of Technology, and Bernstein Center for Computational Neuroscience, Marchstr. 23, 10587 Berlin, Germany.
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48
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Murray JM, Escola GS. Learning multiple variable-speed sequences in striatum via cortical tutoring. eLife 2017; 6. [PMID: 28481200 PMCID: PMC5446244 DOI: 10.7554/elife.26084] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 05/07/2017] [Indexed: 01/16/2023] Open
Abstract
Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain. DOI:http://dx.doi.org/10.7554/eLife.26084.001
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Affiliation(s)
- James M Murray
- Center for Theoretical Neuroscience, Columbia University, New York, United States
| | - G Sean Escola
- Center for Theoretical Neuroscience, Columbia University, New York, United States
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49
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Han R, Wang J, Miao R, Deng B, Qin Y, Yu H, Wei X. Propagation of Collective Temporal Regularity in Noisy Hierarchical Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017; 28:191-205. [PMID: 28055909 DOI: 10.1109/tnnls.2015.2502993] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Neuronal communication between different brain areas is achieved in terms of spikes. Consequently, spike-time regularity is closely related to many cognitive tasks and timing precision of neural information processing. A recent experiment on primate parietal cortex reports that spike-time regularity increases consistently from primary sensory to higher cortical regions. This observation conflicts with the influential view that spikes in the neocortex are fundamentally irregular. To uncover the underlying network mechanism, we construct a multilayered feedforward neural information transmission pathway and investigate how spike-time regularity evolves across subsequent layers. Numerical results reveal that despite the obviously irregular spiking patterns in previous several layers, neurons in downstream layers can generate rather regular spikes, which depends on the network topology. In particular, we find that collective temporal regularity in deeper layers exhibits resonance-like behavior with respect to both synaptic connection probability and synaptic weight, i.e., the optimal topology parameter maximizes the spike-timing regularity. Furthermore, it is demonstrated that synaptic properties, including inhibition, synaptic transient dynamics, and plasticity, have significant impacts on spike-timing regularity propagation. The emergence of the increasingly regular spiking (RS) patterns in higher parietal regions can, thus, be viewed as a natural consequence of spiking activity propagation between different brain areas. Finally, we validate an important function served by increased RS: promoting reliable propagation of spike-rate signals across downstream layers.
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50
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Grajski KA. Emergent Spatial Patterns of Excitatory and Inhibitory Synaptic Strengths Drive Somatotopic Representational Discontinuities and their Plasticity in a Computational Model of Primary Sensory Cortical Area 3b. Front Comput Neurosci 2016; 10:72. [PMID: 27504086 PMCID: PMC4958931 DOI: 10.3389/fncom.2016.00072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 06/29/2016] [Indexed: 11/13/2022] Open
Abstract
Mechanisms underlying the emergence and plasticity of representational discontinuities in the mammalian primary somatosensory cortical representation of the hand are investigated in a computational model. The model consists of an input lattice organized as a three-digit hand forward-connected to a lattice of cortical columns each of which contains a paired excitatory and inhibitory cell. Excitatory and inhibitory synaptic plasticity of feedforward and lateral connection weights is implemented as a simple covariance rule and competitive normalization. Receptive field properties are computed independently for excitatory and inhibitory cells and compared within and across columns. Within digit representational zones intracolumnar excitatory and inhibitory receptive field extents are concentric, single-digit, small, and unimodal. Exclusively in representational boundary-adjacent zones, intracolumnar excitatory and inhibitory receptive field properties diverge: excitatory cell receptive fields are single-digit, small, and unimodal; and the paired inhibitory cell receptive fields are bimodal, double-digit, and large. In simulated syndactyly (webbed fingers), boundary-adjacent intracolumnar receptive field properties reorganize to within-representation type; divergent properties are reacquired following syndactyly release. This study generates testable hypotheses for assessment of cortical laminar-dependent receptive field properties and plasticity within and between cortical representational zones. For computational studies, present results suggest that concurrent excitatory and inhibitory plasticity may underlie novel emergent properties.
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