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Choudhary K, Berberich S, Hahn TTG, McFarland JM, Mehta MR. Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity. Nat Commun 2024; 15:3542. [PMID: 38719802 PMCID: PMC11079062 DOI: 10.1038/s41467-024-47617-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 04/04/2024] [Indexed: 05/12/2024] Open
Abstract
Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.
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Affiliation(s)
- Krishna Choudhary
- Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA, USA
- HRL Laboratories, Malibu, CA, USA
| | - Sven Berberich
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | | | | | - Mayank R Mehta
- Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA, USA.
- W. M. Keck Center for Neurophysics, University of California, Los Angeles, CA, USA.
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
- Departments of Neurology and Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA.
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2
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The essential role of hippocampo-cortical connections in temporal coordination of spindles and ripples. Neuroimage 2021; 243:118485. [PMID: 34425227 DOI: 10.1016/j.neuroimage.2021.118485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 11/22/2022] Open
Abstract
The predominant activity of slow wave sleep is cortical slow oscillations (SOs), thalamic spindles and hippocampal sharp wave ripples. While the precise temporal nesting of these rhythms was shown to be essential for memory consolidation, the coordination mechanism is poorly understood. Here we develop a minimal hippocampo-cortico-thalamic network that can explain the mechanism underlying the SO-spindle-ripple coupling indicating of the succession of regional neuronal interactions. Further we verify the model predictions experimentally in naturally sleeping rodents showing our simple model provides a quantitative match to several experimental observations including the nesting of ripples in the spindle troughs and larger duration but lower amplitude of the ripples co-occurring with spindles or SOs compared to the isolated ripples. The model also predicts that the coupling of ripples to SOs and spindles monotonically enhances by increasing the strength of hippocampo-cortical connections while it is stronger at intermediate values of the cortico-hippocampal projections.
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3
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Foroutannia A, Nazarimehr F, Ghasemi M, Jafari S. Chaos in memory function of sleep: A nonlinear dynamical analysis in thalamocortical study. J Theor Biol 2021; 528:110837. [PMID: 34273361 DOI: 10.1016/j.jtbi.2021.110837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/07/2021] [Accepted: 07/11/2021] [Indexed: 11/30/2022]
Abstract
Studying the dynamical behaviors of neuronal models may help in better understanding of real nervous system. In addition, it can help researchers to understand some specific phenomena in neuronal system. The thalamocortical network is made of neurons in the thalamus and cortex. In it, the memory function is consolidated in sleep by creating up and down state oscillations (1 Hz) and fast (13-17 Hz) - slow (8-12 Hz) spindles. Recently, a nonlinear biological model for up-down oscillations and fast-slow spindles of the thalamocortical network has been proposed. In this research, the power spectral for the fast-slow spindle of the model is extracted. Dynamical properties of the model, such as the bifurcation diagrams, and attractors are investigated. The results show that the variation of the synaptic power between the excitatory neurons of the cortex and the reticular neurons in the thalamus changes the spindles' activity. According to previous experimental findings, it is an essential rule for consolidating the memory function during sleep. It is also pointed out that when the fast-slow spindles of the brain increase, the dynamics of the thalamocortical system tend to chaos.
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Affiliation(s)
- Ali Foroutannia
- Neural Engineering Laboratory, Department of Biomedical Engineering, University of Neyshabur, Neyshabur, Iran
| | - Fahimeh Nazarimehr
- Department of Biomedical Engineering, Amirkabir University of Technology, No. 350, Hafez Ave, Valiasr Square, Tehran 159163-4311, Iran
| | - Mahdieh Ghasemi
- Neural Engineering Laboratory, Department of Biomedical Engineering, University of Neyshabur, Neyshabur, Iran.
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology, No. 350, Hafez Ave, Valiasr Square, Tehran 159163-4311, Iran; Health Technology Research Institute, Amirkabir University of Technology, No. 350, Hafez Ave, Valiasr Square, Tehran 159163-4311, Iran
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4
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Tatsuno M, Malek S, Kalvi L, Ponce-Alvarez A, Ali K, Euston DR, Gruen S, McNaughton BL. Memory reactivation in rat medial prefrontal cortex occurs in a subtype of cortical UP state during slow-wave sleep. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190227. [PMID: 32248781 DOI: 10.1098/rstb.2019.0227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Interaction between hippocampal sharp-wave ripples (SWRs) and UP states, possibly by coordinated reactivation of memory traces, is conjectured to play an important role in memory consolidation. Recently, it was reported that SWRs were differentiated into multiple subtypes. However, whether cortical UP states can also be classified into subtypes is not known. Here, we analysed neural ensemble activity from the medial prefrontal cortex from rats trained to run a spatial sequence-memory task. Application of the hidden Markov model (HMM) with three states to epochs of UP-DOWN oscillations identified DOWN states and two subtypes of UP state (UP-1 and UP-2). The two UP subtypes were distinguished by differences in duration, with UP-1 having a longer duration than UP-2, as well as differences in the speed of population vector (PV) decorrelation, with UP-1 decorrelating more slowly than UP-2. Reactivation of recent memory sequences predominantly occurred in UP-2. Short-duration reactivating UP states were dominated by UP-2 whereas long-duration ones exhibit transitions from UP-1 to UP-2. Thus, recent memory reactivation, if it occurred within long-duration UP states, typically was preceded by a period of slow PV evolution not related to recent experience, and which we speculate may be related to previously encoded information. If that is the case, then the transition from UP-1 to UP-2 subtypes may help gradual integration of recent experience with pre-existing cortical memories by interleaving the two in the same UP state. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.
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Affiliation(s)
- Masami Tatsuno
- Department of Neuroscience, University of Lethbridge, Lethbridge, T1K 3M4 Alberta, Canada
| | - Soroush Malek
- Department of Neuroscience, University of Lethbridge, Lethbridge, T1K 3M4 Alberta, Canada
| | - LeAnna Kalvi
- Department of Neuroscience, University of Lethbridge, Lethbridge, T1K 3M4 Alberta, Canada.,Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, T6G 2H7 Alberta, Canada
| | - Adrian Ponce-Alvarez
- Center for Brain and Cognition, Computational Neuroscience Group, Pompeu Fabra University, 08005 Barcelona, Spain
| | - Karim Ali
- Department of Neuroscience, University of Lethbridge, Lethbridge, T1K 3M4 Alberta, Canada
| | - David R Euston
- Department of Neuroscience, University of Lethbridge, Lethbridge, T1K 3M4 Alberta, Canada
| | - Sonja Gruen
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Brain Institute I (INM-10), Jülich Research Center, 52425 Jülich, Germany.,Theoretical Systems Neurobiology, RWTH Aachen University, 52056 Aachen, Germany
| | - Bruce L McNaughton
- Department of Neuroscience, University of Lethbridge, Lethbridge, T1K 3M4 Alberta, Canada.,Department of Neurobiology and Behaviour, University of California, Irvine, CA 92697, USA
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5
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Levenstein D, Buzsáki G, Rinzel J. NREM sleep in the rodent neocortex and hippocampus reflects excitable dynamics. Nat Commun 2019; 10:2478. [PMID: 31171779 PMCID: PMC6554409 DOI: 10.1038/s41467-019-10327-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 04/24/2019] [Indexed: 01/10/2023] Open
Abstract
During non-rapid eye movement (NREM) sleep, neuronal populations in the mammalian forebrain alternate between periods of spiking and inactivity. Termed the slow oscillation in the neocortex and sharp wave-ripples in the hippocampus, these alternations are often considered separately but are both crucial for NREM functions. By directly comparing experimental observations of naturally-sleeping rats with a mean field model of an adapting, recurrent neuronal population, we find that the neocortical alternations reflect a dynamical regime in which a stable active state is interrupted by transient inactive states (slow waves) while the hippocampal alternations reflect a stable inactive state interrupted by transient active states (sharp waves). We propose that during NREM sleep in the rodent, hippocampal and neocortical populations are excitable: each in a stable state from which internal fluctuations or external perturbation can evoke the stereotyped population events that mediate NREM functions.
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Affiliation(s)
- Daniel Levenstein
- Center for Neural Science, New York University, 4 Washington Pl, New York, NY, 10003, USA.,NYU Neuroscience Institute, 450 East 29th Street, New York, NY, 10016, USA
| | - György Buzsáki
- Center for Neural Science, New York University, 4 Washington Pl, New York, NY, 10003, USA.,NYU Neuroscience Institute, 450 East 29th Street, New York, NY, 10016, USA
| | - John Rinzel
- Center for Neural Science, New York University, 4 Washington Pl, New York, NY, 10003, USA. .,Courant Institute for Mathematical Sciences, New York University, 251 Mercer St, New York, 10012, USA.
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6
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Pena RFO, Zaks MA, Roque AC. Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise : Spontaneous activity in networks with synaptic noise. J Comput Neurosci 2018; 45:1-28. [PMID: 29923159 PMCID: PMC6061197 DOI: 10.1007/s10827-018-0688-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 05/19/2018] [Accepted: 05/23/2018] [Indexed: 12/18/2022]
Abstract
Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.
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Affiliation(s)
- Rodrigo F. O. Pena
- Department of Physics, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP Brazil
| | - Michael A. Zaks
- Department of Physics, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin, Berlin, Germany
| | - Antonio C. Roque
- Department of Physics, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP Brazil
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7
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Jercog D, Roxin A, Barthó P, Luczak A, Compte A, de la Rocha J. UP-DOWN cortical dynamics reflect state transitions in a bistable network. eLife 2017; 6:22425. [PMID: 28826485 PMCID: PMC5582872 DOI: 10.7554/elife.22425] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 07/21/2017] [Indexed: 11/21/2022] Open
Abstract
In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests.
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Affiliation(s)
- Daniel Jercog
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Alex Roxin
- Centre de Recerca Matemàtica, Bellaterra, Spain
| | - Peter Barthó
- MTA TTK NAP B Research Group of Sleep Oscillations, Budapest, Hungary
| | - Artur Luczak
- Canadian Center for Behavioural Neuroscience, University of Lethbridge, Lethbridge, Canada
| | - Albert Compte
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Jaime de la Rocha
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
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8
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Setareh H, Deger M, Petersen CCH, Gerstner W. Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons. Front Comput Neurosci 2017; 11:52. [PMID: 28690508 PMCID: PMC5480278 DOI: 10.3389/fncom.2017.00052] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 05/29/2017] [Indexed: 01/21/2023] Open
Abstract
Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data. Simulations of such a network with generalized integrate-and-fire neurons display regular and irregular slow oscillations akin to experimentally observed up/down state transitions in the activity of cortical neurons with a broad distribution of pairwise spike correlations. Moreover, stimulation of a model network in the presence or absence of assembly structure exhibits responses similar to light-evoked responses of cortical layers in optogenetically modified animals. We conclude that a high connection probability into and within assemblies of excitatory weight-hub neurons, as it likely is present in some but not all cortical layers, changes the dynamics of a layer of cortical microcircuitry significantly.
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Affiliation(s)
- Hesam Setareh
- Laboratory of Computational Neuroscience, School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Moritz Deger
- Laboratory of Computational Neuroscience, School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de LausanneLausanne, Switzerland.,Faculty of Mathematics and Natural Sciences, Institute for Zoology, University of CologneCologne, Germany
| | - Carl C H Petersen
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Wulfram Gerstner
- Laboratory of Computational Neuroscience, School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de LausanneLausanne, Switzerland
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9
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Nonstationary Stochastic Dynamics Underlie Spontaneous Transitions between Active and Inactive Behavioral States. eNeuro 2017; 4:eN-NWR-0355-16. [PMID: 28374017 PMCID: PMC5370279 DOI: 10.1523/eneuro.0355-16.2017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 02/16/2017] [Accepted: 02/22/2017] [Indexed: 11/21/2022] Open
Abstract
The neural basis of spontaneous movement generation is a fascinating open question. Long-term monitoring of fish, swimming freely in a constant sensory environment, has revealed a sequence of behavioral states that alternate randomly and spontaneously between periods of activity and inactivity. We show that key dynamical features of this sequence are captured by a 1-D diffusion process evolving in a nonlinear double well energy landscape, in which a slow variable modulates the relative depth of the wells. This combination of stochasticity, nonlinearity, and nonstationary forcing correctly captures the vastly different timescales of fluctuations observed in the data (∼1 to ∼1000 s), and yields long-tailed residence time distributions (RTDs) also consistent with the data. In fact, our model provides a simple mechanism for the emergence of long-tailed distributions in spontaneous animal behavior. We interpret the stochastic variable of this dynamical model as a decision-like variable that, upon reaching a threshold, triggers the transition between states. Our main finding is thus the identification of a threshold crossing process as the mechanism governing spontaneous movement initiation and termination, and to infer the presence of underlying nonstationary agents. Another important outcome of our work is a dimensionality reduction scheme that allows similar segments of data to be grouped together. This is done by first extracting geometrical features in the dataset and then applying principal component analysis over the feature space. Our study is novel in its ability to model nonstationary behavioral data over a wide range of timescales.
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10
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Weigenand A, Schellenberger Costa M, Ngo HVV, Claussen JC, Martinetz T. Characterization of K-complexes and slow wave activity in a neural mass model. PLoS Comput Biol 2014; 10:e1003923. [PMID: 25392991 PMCID: PMC4230734 DOI: 10.1371/journal.pcbi.1003923] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2014] [Accepted: 09/19/2014] [Indexed: 11/18/2022] Open
Abstract
NREM sleep is characterized by two hallmarks, namely K-complexes (KCs) during sleep stage N2 and cortical slow oscillations (SOs) during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep.
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Affiliation(s)
- Arne Weigenand
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
| | - Michael Schellenberger Costa
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Hong-Viet Victor Ngo
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Jens Christian Claussen
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Computational Systems Biology, Jacobs University Bremen, Bremen, Germany
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
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11
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Azizi AH, Wiskott L, Cheng S. A computational model for preplay in the hippocampus. Front Comput Neurosci 2013; 7:161. [PMID: 24282402 PMCID: PMC3824291 DOI: 10.3389/fncom.2013.00161] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Accepted: 10/22/2013] [Indexed: 01/20/2023] Open
Abstract
The hippocampal network produces sequences of neural activity even when there is no time-varying external drive. In offline states, the temporal sequence in which place cells fire spikes correlates with the sequence of their place fields. Recent experiments found this correlation even between offline sequential activity (OSA) recorded before the animal ran in a novel environment and the place fields in that environment. This preplay phenomenon suggests that OSA is generated intrinsically in the hippocampal network, and not established by external sensory inputs. Previous studies showed that continuous attractor networks with asymmetric patterns of connectivity, or with slow, local negative feedback, can generate sequential activity. This mechanism could account for preplay if the network only represented a single spatial map, or chart. However, global remapping in the hippocampus implies that multiple charts are represented simultaneously in the hippocampal network and it remains unknown whether the network with multiple charts can account for preplay. Here we show that it can. Driven with random inputs, the model generates sequences in every chart. Place fields in a given chart and OSA generated by the network are highly correlated. We also find significant correlations, albeit less frequently, even when the OSA is correlated with a new chart in which place fields are randomly scattered. These correlations arise from random correlations between the orderings of place fields in the new chart and those in a pre-existing chart. Our results suggest two different accounts for preplay. Either an existing chart is re-used to represent a novel environment or a new chart is formed.
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Affiliation(s)
- Amir H Azizi
- Mercator Research Group "Structure of Memory," Department of Psychology, Ruhr-University Bochum Bochum, Germany
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12
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Spontaneous persistent activity in entorhinal cortex modulates cortico-hippocampal interaction in vivo. Nat Neurosci 2012; 15:1531-8. [PMID: 23042081 DOI: 10.1038/nn.3236] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2012] [Accepted: 09/12/2012] [Indexed: 12/22/2022]
Abstract
Persistent activity is thought to mediate working memory during behavior. Can it also occur during sleep? We found that the membrane potential of medial entorhinal cortex layer III (MECIII) neurons, a gateway between neocortex and hippocampus, showed spontaneous, stochastic persistent activity in vivo in mice during Up-Down state oscillations (UDS). This persistent activity was locked to the neocortical Up states with a short delay, but persisted over several cortical UDS cycles. Lateral entorhinal neurons did not show substantial persistence, and current injections similar to those used in vitro failed to elicit persistence in vivo, implicating network mechanisms. Hippocampal CA1 neurons' spiking activity was reduced during neocortical Up states, but was increased during MECIII persistent states. These results provide, to the best of our knowledge, the first direct evidence for persistent activity in MECIII neurons in vivo and reveal its contribution to cortico-hippocampal interaction that could be involved in working memory and learning of long behavioral sequences during behavior, and memory consolidation during sleep.
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