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Marsh B, Navas-Zuloaga MG, Rosen BQ, Sokolov Y, Delanois JE, Gonzalez OC, Krishnan GP, Halgren E, Bazhenov M. Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain. PLoS Comput Biol 2024; 20:e1012245. [PMID: 39028760 DOI: 10.1371/journal.pcbi.1012245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 06/11/2024] [Indexed: 07/21/2024] Open
Abstract
Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.
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Affiliation(s)
- Brianna Marsh
- Department of Medicine, University of California San Diego, San Diego, California, United States of America
- Neuroscience Graduate Program, University of California San Diego, San Diego, California, United States of America
| | - M Gabriela Navas-Zuloaga
- Department of Medicine, University of California San Diego, San Diego, California, United States of America
| | - Burke Q Rosen
- Neuroscience Graduate Program, University of California San Diego, San Diego, California, United States of America
| | - Yury Sokolov
- Department of Medicine, University of California San Diego, San Diego, California, United States of America
| | - Jean Erik Delanois
- Department of Medicine, University of California San Diego, San Diego, California, United States of America
- Department of Computer Science and Engineering, University of California San Diego, San Diego, California, United States of America
| | - Oscar C Gonzalez
- Department of Medicine, University of California San Diego, San Diego, California, United States of America
| | - Giri P Krishnan
- Department of Medicine, University of California San Diego, San Diego, California, United States of America
| | - Eric Halgren
- Neuroscience Graduate Program, University of California San Diego, San Diego, California, United States of America
- Departments of Radiology and Neuroscience, University of California San Diego, San Diego, California, United States of America
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, San Diego, California, United States of America
- Neuroscience Graduate Program, University of California San Diego, San Diego, California, United States of America
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Marsh BM, Navas-Zuloaga MG, Rosen BQ, Sokolov Y, Delanois JE, González OC, Krishnan GP, Halgren E, Bazhenov M. Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.15.562408. [PMID: 38617301 PMCID: PMC11014475 DOI: 10.1101/2023.10.15.562408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.
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Affiliation(s)
- Brianna M Marsh
- Department of Medicine, University of California, San Diego
- Neuroscience Graduate Program, University of California, San Diego
| | | | - Burke Q Rosen
- Neuroscience Graduate Program, University of California, San Diego
| | - Yury Sokolov
- Department of Medicine, University of California, San Diego
| | - Jean Erik Delanois
- Department of Medicine, University of California, San Diego
- Department of Computer Science and Engineering, University of California, San Diego
| | | | | | - Eric Halgren
- Neuroscience Graduate Program, University of California, San Diego
- Department of Radiology and Neuroscience, University of California, San Diego
| | - Maxim Bazhenov
- Department of Medicine, University of California, San Diego
- Neuroscience Graduate Program, University of California, San Diego
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Kim B, Haney S, Milan AP, Joshi S, Aldworth Z, Rulkov N, Kim AT, Bazhenov M, Stopfer MA. Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality. eLife 2023; 12:79152. [PMID: 36719272 PMCID: PMC9925048 DOI: 10.7554/elife.79152] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 01/31/2023] [Indexed: 02/01/2023] Open
Abstract
Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed that odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plume's composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated that these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.
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Affiliation(s)
- Brian Kim
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
- Brown University - National Institutes of Health Graduate Partnership ProgramProvidenceUnited States
| | - Seth Haney
- Department of Medicine, University of California, San DiegoSan DiegoUnited States
| | - Ana P Milan
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Shruti Joshi
- Department of Medicine, University of California, San DiegoSan DiegoUnited States
| | - Zane Aldworth
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
| | - Nikolai Rulkov
- Biocircuits Institute, University of California, San DiegoLa JollaUnited States
| | - Alexander T Kim
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
| | - Maxim Bazhenov
- Department of Medicine, University of California, San DiegoSan DiegoUnited States
| | - Mark A Stopfer
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
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Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation. PLoS Comput Biol 2022; 18:e1010628. [PMID: 36399437 PMCID: PMC9674146 DOI: 10.1371/journal.pcbi.1010628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/03/2022] [Indexed: 11/19/2022] Open
Abstract
Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.
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Filippov VA, Bobylev AN, Busygin AN, Pisarev AD, Udovichenko SY. A biomorphic neuron model and principles of designing a neural network with memristor synapses for a biomorphic neuroprocessor. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04383-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Malerba P, Rulkov NF, Bazhenov M. Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3. COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION 2019; 72:162-175. [PMID: 33814862 PMCID: PMC8015963 DOI: 10.1016/j.cnsns.2018.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Reduced models of neuronal spiking activity simulated with a fixed integration time are frequently used in studies of spatio-temporal dynamics of neurobiological networks. The choice of fixed time step integration provides computational simplicity and efficiency, especially in cases dealing with large number of neurons and synapses operating at a different level of activity across the population at any given time. A network model tuned to generate a particular type of oscillations or wave patterns is sensitive to the intrinsic properties of neurons and synapses and, therefore, commonly susceptible to changes the time step of integration. In this study, we analyzed a model of sharp-wave activity in the network of hippocampal area CA3, to examine how an increase of the integration time step affects network behavior and to propose adjustments of intrinsic properties neurons and synapses that help minimize or remove the damage caused by the time step increase.
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Affiliation(s)
- Paola Malerba
- Department of Medicine, University of California San Diego,
9500 Gilman Drive, La Jolla, CA 92093, United States
- Department of Cognitive Sciences, University of California
Irvine, Irvine, CA 92697-5100, United States
| | - Nikolai F. Rulkov
- BioCircuits Institute, University of California San Diego,
9500 Gilman Drive, La Jolla, CA 92093, United States
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego,
9500 Gilman Drive, La Jolla, CA 92093, United States
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Rosen BQ, Krishnan GP, Sanda P, Komarov M, Sejnowski T, Rulkov N, Ulbert I, Eross L, Madsen J, Devinsky O, Doyle W, Fabo D, Cash S, Bazhenov M, Halgren E. Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics. J Neurosci Methods 2019; 316:46-57. [PMID: 30300700 PMCID: PMC6380919 DOI: 10.1016/j.jneumeth.2018.10.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 10/03/2018] [Accepted: 10/04/2018] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. METHOD We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. RESULTS The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. COMPARISON WITH EXISTING METHODS Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales. CONCLUSIONS This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.
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Affiliation(s)
- B Q Rosen
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States.
| | - G P Krishnan
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States.
| | - P Sanda
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States; Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic.
| | - M Komarov
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States.
| | - T Sejnowski
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; The Salk Institute, La Jolla, CA, United States.
| | - N Rulkov
- BioCiruits Institute, University of California, San Diego, La Jolla, CA, United States.
| | - I Ulbert
- Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Science, Budapest, Hungary; Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, Budapest, Hungary.
| | - L Eross
- Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, Budapest, Hungary; Department of Functional Neurosurgery, National Institute of Clinical Neurosciences, Budapest, Hungary.
| | - J Madsen
- Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.
| | - O Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, United States.
| | - W Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, United States.
| | - D Fabo
- Epilepsy Centrum, National Institute of Clinical Neurosciences, Budapest, Hungary.
| | - S Cash
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Medicine, University of California, San Diego, La Jolla, CA, United States; Departments of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
| | - M Bazhenov
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Medicine, University of California, San Diego, La Jolla, CA, United States.
| | - E Halgren
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Radiology, University of California, San Diego, La Jolla, CA, United States; Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States.
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Abstract
Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.
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Affiliation(s)
- Svetlana Postnova
- School of Physics, University of Sydney, Sydney 2006, NSW, Australia;
- Center of Excellence for Integrative Brain Function, University of Sydney, Sydney 2006, NSW, Australia
- Charles Perkins Center, University of Sydney, Sydney 2006, NSW, Australia
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Rennó-Costa C, da Silva ACC, Blanco W, Ribeiro S. Computational models of memory consolidation and long-term synaptic plasticity during sleep. Neurobiol Learn Mem 2018; 160:32-47. [PMID: 30321652 DOI: 10.1016/j.nlm.2018.10.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 09/18/2018] [Accepted: 10/11/2018] [Indexed: 12/19/2022]
Abstract
The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings - and therefore the theories - are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.
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Affiliation(s)
- César Rennó-Costa
- BioMe - Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil; Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ana Cláudia Costa da Silva
- BioMe - Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil; Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil; Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil; Federal University of Paraiba, João Pessoa, Brazil
| | - Wilfredo Blanco
- BioMe - Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil; Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil; State University of Rio Grande do Norte, Natal, Brazil
| | - Sidarta Ribeiro
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil.
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