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Mahuas G, Marre O, Mora T, Ferrari U. Small-correlation expansion to quantify information in noisy sensory systems. Phys Rev E 2023; 108:024406. [PMID: 37723816 DOI: 10.1103/physreve.108.024406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 06/26/2023] [Indexed: 09/20/2023]
Abstract
Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with a complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations. Here, we develop a small-correlation expansion to compute the stimulus information carried by a large population of neurons, yielding interpretable analytical expressions in terms of the neurons' firing rates and pairwise correlations. We validate the approximation on synthetic data and demonstrate its applicability to electrophysiological recordings in the vertebrate retina, allowing us to quantify the effects of noise correlations between neurons and of memory in single neurons.
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Affiliation(s)
- Gabriel Mahuas
- Institut de la Vision, Sorbonne Université, CNRS, INSERM, 17 rue Moreau, 75012 Paris, France
- Laboratoire de Physique de École Normale Supérieure, CNRS, PSL University, Sorbonne University, Université Paris-Cité, 24 rue Lhomond, 75005 Paris, France
| | - Olivier Marre
- Institut de la Vision, Sorbonne Université, CNRS, INSERM, 17 rue Moreau, 75012 Paris, France
| | - Thierry Mora
- Laboratoire de Physique de École Normale Supérieure, CNRS, PSL University, Sorbonne University, Université Paris-Cité, 24 rue Lhomond, 75005 Paris, France
| | - Ulisse Ferrari
- Institut de la Vision, Sorbonne Université, CNRS, INSERM, 17 rue Moreau, 75012 Paris, France
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2
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Alemanno F, Cavo M, Delle Cave D, Fachechi A, Rizzo R, D’Amone E, Gigli G, Lonardo E, Barra A, del Mercato LL. Quantifying heterogeneity to drug response in cancer-stroma kinetics. Proc Natl Acad Sci U S A 2023; 120:e2122352120. [PMID: 36897966 PMCID: PMC10089157 DOI: 10.1073/pnas.2122352120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/04/2023] [Indexed: 03/12/2023] Open
Abstract
A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.
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Affiliation(s)
- Francesco Alemanno
- Institute of Nanotechnology, National Research Council, Lecce73100, Italy
- Dipartimento di Matematica e Fisica Ennio De Giorgi, Università del Salento, Lecce73100, Italy
| | - Marta Cavo
- Institute of Nanotechnology, National Research Council, Lecce73100, Italy
| | - Donatella Delle Cave
- Institute of Genetics and Biophysics Adriano Buzzati-Traverso, CNR, Naples80131, Italy
| | - Alberto Fachechi
- Dipartimento di Matematica Guido Castelnuovo, Sapienza Università di Roma, Rome00185, Italy
| | - Riccardo Rizzo
- Institute of Nanotechnology, National Research Council, Lecce73100, Italy
| | - Eliana D’Amone
- Institute of Nanotechnology, National Research Council, Lecce73100, Italy
| | - Giuseppe Gigli
- Institute of Nanotechnology, National Research Council, Lecce73100, Italy
- Dipartimento di Matematica e Fisica Ennio De Giorgi, Università del Salento, Lecce73100, Italy
| | - Enza Lonardo
- Institute of Genetics and Biophysics Adriano Buzzati-Traverso, CNR, Naples80131, Italy
| | - Adriano Barra
- Dipartimento di Matematica e Fisica Ennio De Giorgi, Università del Salento, Lecce73100, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Lecce, Lecce73100, Italy
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3
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van der Plas TL, Tubiana J, Le Goc G, Migault G, Kunst M, Baier H, Bormuth V, Englitz B, Debrégeas G. Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity. eLife 2023; 12:83139. [PMID: 36648065 PMCID: PMC9940913 DOI: 10.7554/elife.83139] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/15/2023] [Indexed: 01/18/2023] Open
Abstract
Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet, it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here, we recorded the activity from ∼40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ∼200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.
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Affiliation(s)
- Thijs L van der Plas
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Center for Neuroscience, Radboud UniversityNijmegenNetherlands
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
- Department of Physiology, Anatomy and Genetics, University of OxfordOxfordUnited Kingdom
| | - Jérôme Tubiana
- Blavatnik School of Computer Science, Tel Aviv UniversityTel AvivIsrael
| | - Guillaume Le Goc
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
| | - Geoffrey Migault
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
| | - Michael Kunst
- Department Genes – Circuits – Behavior, Max Planck Institute for Biological IntelligenceMartinsriedGermany
- Allen Institute for Brain ScienceSeattleUnited States
| | - Herwig Baier
- Department Genes – Circuits – Behavior, Max Planck Institute for Biological IntelligenceMartinsriedGermany
| | - Volker Bormuth
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
| | - Bernhard Englitz
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Center for Neuroscience, Radboud UniversityNijmegenNetherlands
| | - Georges Debrégeas
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
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4
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Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model. Sci Rep 2022; 12:19339. [PMID: 36369262 PMCID: PMC9652375 DOI: 10.1038/s41598-022-23770-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
A common issue when analyzing real-world complex systems is that the interactions between their elements often change over time. Here we propose a new modeling approach for time-varying interactions generalising the well-known Kinetic Ising Model, a minimalistic pairwise constant interactions model which has found applications in several scientific disciplines. Keeping arbitrary choices of dynamics to a minimum and seeking information theoretical optimality, the Score-Driven methodology allows to extract from data and interpret the presence of temporal patterns describing time-varying interactions. We identify a parameter whose value at a given time can be directly associated with the local predictability of the dynamics and we introduce a method to dynamically learn its value from the data, without specifying parametrically the system's dynamics. We extend our framework to disentangle different sources (e.g. endogenous vs exogenous) of predictability in real time, and show how our methodology applies to a variety of complex systems such as financial markets, temporal (social) networks, and neuronal populations.
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5
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Nghiem TAE, Tort-Colet N, Górski T, Ferrari U, Moghimyfiroozabad S, Goldman JS, Teleńczuk B, Capone C, Bal T, di Volo M, Destexhe A. Cholinergic Switch between Two Types of Slow Waves in Cerebral Cortex. Cereb Cortex 2020; 30:3451-3466. [DOI: 10.1093/cercor/bhz320] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 01/17/2023] Open
Abstract
Abstract
Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory versus excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases.
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Affiliation(s)
- Trang-Anh E Nghiem
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
- Laboratory of Physics, Department of Physics, Ecole Normale Supérieure, 75005 Paris, France
| | - Núria Tort-Colet
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Tomasz Górski
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Ulisse Ferrari
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 75012 Paris, France
| | - Shayan Moghimyfiroozabad
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Jennifer S Goldman
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Bartosz Teleńczuk
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Cristiano Capone
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
- Istituto Nazionale di Fisica Nucleare Sezione di Roma, 00185 Rome, Italy
| | - Thierry Bal
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Matteo di Volo
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
| | - Alain Destexhe
- Department of Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique (CNRS), 91190 Gif-sur-Yvette, France
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6
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Goldman JS, Tort-Colet N, di Volo M, Susin E, Bouté J, Dali M, Carlu M, Nghiem TA, Górski T, Destexhe A. Bridging Single Neuron Dynamics to Global Brain States. Front Syst Neurosci 2019; 13:75. [PMID: 31866837 PMCID: PMC6908479 DOI: 10.3389/fnsys.2019.00075] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 11/19/2019] [Indexed: 11/13/2022] Open
Abstract
Biological neural networks produce information backgrounds of multi-scale spontaneous activity that become more complex in brain states displaying higher capacities for cognition, for instance, attentive awake versus asleep or anesthetized states. Here, we review brain state-dependent mechanisms spanning ion channel currents (microscale) to the dynamics of brain-wide, distributed, transient functional assemblies (macroscale). Not unlike how microscopic interactions between molecules underlie structures formed in macroscopic states of matter, using statistical physics, the dynamics of microscopic neural phenomena can be linked to macroscopic brain dynamics through mesoscopic scales. Beyond spontaneous dynamics, it is observed that stimuli evoke collapses of complexity, most remarkable over high dimensional, asynchronous, irregular background dynamics during consciousness. In contrast, complexity may not be further collapsed beyond synchrony and regularity characteristic of unconscious spontaneous activity. We propose that increased dimensionality of spontaneous dynamics during conscious states supports responsiveness, enhancing neural networks' emergent capacity to robustly encode information over multiple scales.
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Affiliation(s)
- Jennifer S. Goldman
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Núria Tort-Colet
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Matteo di Volo
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Eduarda Susin
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Jules Bouté
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Melissa Dali
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Mallory Carlu
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | | | - Tomasz Górski
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
| | - Alain Destexhe
- Department of Integrative and Computational Neuroscience (ICN), Centre National de la Recherche Scientifique (CNRS), Paris-Saclay Institute of Neuroscience (NeuroPSI), Gif-sur-Yvette, France
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7
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Zanoci C, Dehghani N, Tegmark M. Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties. Phys Rev E 2019; 99:032408. [PMID: 30999501 DOI: 10.1103/physreve.99.032408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Indexed: 11/07/2022]
Abstract
The pairwise maximum entropy model, also known as the Ising model, has been widely used to analyze the collective activity of neurons. However, controversy persists in the literature about seemingly inconsistent findings, whose significance is unclear due to lack of reliable error estimates. We therefore develop a method for accurately estimating parameter uncertainty based on random walks in parameter space using adaptive Markov-chain Monte Carlo after the convergence of the main optimization algorithm. We apply our method to the activity patterns of excitatory and inhibitory neurons recorded with multielectrode arrays in the human temporal cortex during the wake-sleep cycle. Our analysis shows that the Ising model captures neuronal collective behavior much better than the independent model during wakefulness, light sleep, and deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled; ignoring the inhibitory effects of I neurons dramatically overestimates synchrony among E neurons. Furthermore, information-theoretic measures reveal that the Ising model explains about 80-95% of the correlations, depending on sleep state and neuron type. Thermodynamic measures show signatures of criticality, although we take this with a grain of salt as it may be merely a reflection of long-range neural correlations.
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Affiliation(s)
- Cristian Zanoci
- Department of Physics and Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Nima Dehghani
- Department of Physics and Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Max Tegmark
- Department of Physics and Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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8
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Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning. J Neurosci 2019; 39:3470-3483. [PMID: 30814311 DOI: 10.1523/jneurosci.1370-17.2019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 01/09/2019] [Accepted: 01/11/2019] [Indexed: 11/21/2022] Open
Abstract
The prefrontal cortex (PFC) is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in PFC are specific to learning these relationships. Here we characterize the plasticity of population activity in the medial PFC (mPFC) of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, regardless of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and nonlearning sessions. In learning sessions, the changes in population activity in post-training sleep incorporate the changes to the population activity during training on the maze. In nonlearning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the nonlearning and learning forms of population plasticity are driven by different neuron-level changes, with the nonlearning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in mPFC during the learning of action-outcome relationships: one a persistent change in population activity structure decoupled from overt rule-learning, and the other a directional change driven by feedback during behavior.SIGNIFICANCE STATEMENT The PFC is thought to represent our knowledge about what action is worth doing in which context. But we do not know how the activity of neurons in PFC collectively changes when learning which actions are relevant. Here we show, in a trial-and-error task, that population activity in PFC is persistently changing, regardless of learning. Only during episodes of clear learning of relevant actions are the accompanying changes to population activity carried forward into sleep, suggesting a long-lasting form of neural plasticity. Our results suggest that representations of relevant actions in PFC are acquired by reward imposing a direction onto ongoing population plasticity.
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9
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Nghiem TA, Telenczuk B, Marre O, Destexhe A, Ferrari U. Maximum-entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity. Phys Rev E 2018; 98:012402. [PMID: 30110850 DOI: 10.1103/physreve.98.012402] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Indexed: 01/20/2023]
Abstract
Maximum entropy models can be inferred from large datasets to uncover how collective dynamics emerge from local interactions. Here, such models are employed to investigate neurons recorded by multi-electrode arrays in the human and monkey cortex. Taking advantage of the separation of excitatory and inhibitory neuron types, we construct a model including this distinction. This approach allows us to shed light on differences between excitatory and inhibitory activity across different brain states such as wakefulness and deep sleep, in agreement with previous findings. Additionally, maximum entropy models can also unveil novel features of neuronal interactions, which are found to be dominated by pairwise interactions during wakefulness, but are population-wide during deep sleep. Overall, we demonstrate that maximum entropy models can be useful to analyze datasets with classified neuron types and to reveal the respective roles of excitatory and inhibitory neurons in organizing coherent dynamics in the cerebral cortex.
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Affiliation(s)
- Trang-Anh Nghiem
- Laboratory of Computational Neuroscience, Unité de Neurosciences, Information et Complexité, CNRS, Gif-Sur-Yvette, France
| | - Bartosz Telenczuk
- Laboratory of Computational Neuroscience, Unité de Neurosciences, Information et Complexité, CNRS, Gif-Sur-Yvette, France
| | - Olivier Marre
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France
| | - Alain Destexhe
- Laboratory of Computational Neuroscience, Unité de Neurosciences, Information et Complexité, CNRS, Gif-Sur-Yvette, France
| | - Ulisse Ferrari
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France
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10
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Grossberger L, Battaglia FP, Vinck M. Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. PLoS Comput Biol 2018; 14:e1006283. [PMID: 29979681 PMCID: PMC6051652 DOI: 10.1371/journal.pcbi.1006283] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/18/2018] [Accepted: 06/08/2018] [Indexed: 11/18/2022] Open
Abstract
Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern "noise" spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.
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Affiliation(s)
- Lukas Grossberger
- Donders Institute for Brain, Cognition and Behaviour, Radboud Universiteit, Nijmegen, the Netherlands
- Ernst Strüngmann Institute for Neuroscience in cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - Francesco P. Battaglia
- Donders Institute for Brain, Cognition and Behaviour, Radboud Universiteit, Nijmegen, the Netherlands
| | - Martin Vinck
- Ernst Strüngmann Institute for Neuroscience in cooperation with Max Planck Society, Frankfurt am Main, Germany
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11
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Abstract
The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli.
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Affiliation(s)
- Christophe Gardella
- Laboratoire de physique statistique, Centre National de la Recherche Scientifique, Sorbonne University, University Paris-Diderot, École normale supérieure, PSL University, 75005 Paris, France
- Institut de la Vision, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Sorbonne University, 75012 Paris, France
| | - Olivier Marre
- Institut de la Vision, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Sorbonne University, 75012 Paris, France
| | - Thierry Mora
- Laboratoire de physique statistique, Centre National de la Recherche Scientifique, Sorbonne University, University Paris-Diderot, École normale supérieure, PSL University, 75005 Paris, France;
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12
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Tavoni G, Ferrari U, Battaglia FP, Cocco S, Monasson R. Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity. Netw Neurosci 2017; 1:275-301. [PMID: 29855621 PMCID: PMC5874136 DOI: 10.1162/netn_a_00014] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 04/24/2017] [Indexed: 01/28/2023] Open
Abstract
Functional coupling networks are widely used to characterize collective patterns of activity in neural populations. Here, we ask whether functional couplings reflect the subtle changes, such as in physiological interactions, believed to take place during learning. We infer functional network models reproducing the spiking activity of simultaneously recorded neurons in prefrontal cortex (PFC) of rats, during the performance of a cross-modal rule shift task (task epoch), and during preceding and following sleep epochs. A large-scale study of the 96 recorded sessions allows us to detect, in about 20% of sessions, effective plasticity between the sleep epochs. These coupling modifications are correlated with the coupling values in the task epoch, and are supported by a small subset of the recorded neurons, which we identify by means of an automatized procedure. These potentiated groups increase their coativation frequency in the spiking data between the two sleep epochs, and, hence, participate to putative experience-related cell assemblies. Study of the reactivation dynamics of the potentiated groups suggests a possible connection with behavioral learning. Reactivation is largely driven by hippocampal ripple events when the rule is not yet learned, and may be much more autonomous, and presumably sustained by the potentiated PFC network, when learning is consolidated. Cell assemblies coding for memories are widely believed to emerge through synaptic modification resulting from learning, yet their identification from activity is very arduous. We propose a functional-connectivity-based approach to identify experience-related cell assemblies from multielectrode recordings in vivo, and apply it to the prefrontal cortex activity of rats recorded during a task epoch and the preceding and following sleep epochs. We infer functional couplings between the recorded cells in each epoch. Comparisons of the functional coupling networks across the epochs allow us to identify effective potentiation between the two sleep epochs. The neurons supporting these potentiated interactions strongly coactivate during the task and subsequent sleep epochs, but not in the preceding sleep, and, hence, presumably belong to an experience-related cell assembly. Study of the reactivation of this assembly in response to hippocampal ripple inputs suggests possible relations between the stage of behavorial learning and memory consolidation mechanisms.
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Affiliation(s)
- Gaia Tavoni
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research and CNRS - UMR 8550, Paris Sorbonne UPMC, Paris, France.,Laboratoire de Physique Théorique, Ecole Normale Supérieure, PSL Research and CNRS- UMR 8549, Paris Sorbonne UPMC, Paris, France
| | - Ulisse Ferrari
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research and CNRS - UMR 8550, Paris Sorbonne UPMC, Paris, France.,Laboratoire de Physique Théorique, Ecole Normale Supérieure, PSL Research and CNRS- UMR 8549, Paris Sorbonne UPMC, Paris, France
| | - Francesco P Battaglia
- Donders Institute for Brain, Cognition and Behaviour, Radboud Universiteit, Nijmegen, The Netherlands
| | - Simona Cocco
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research and CNRS - UMR 8550, Paris Sorbonne UPMC, Paris, France
| | - Rémi Monasson
- Laboratoire de Physique Théorique, Ecole Normale Supérieure, PSL Research and CNRS- UMR 8549, Paris Sorbonne UPMC, Paris, France
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