1
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Champaud JLY, Asite S, Fabrizi L. Development of brain metastable dynamics during the equivalent of the third gestational trimester. Dev Cogn Neurosci 2025; 73:101556. [PMID: 40252359 PMCID: PMC12023897 DOI: 10.1016/j.dcn.2025.101556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2024] [Revised: 03/13/2025] [Accepted: 03/14/2025] [Indexed: 04/21/2025] Open
Abstract
Metastability, a concept from dynamical systems theory, provides a framework for understanding how the brain shifts between various functional states and underpins essential cognitive, behavioural, and social function. While studied in adults, metastability in early brain development has only received recent attention. As the brain undergoes dramatic functional and structural changes over the third gestational trimester, here we review how these are reflected in changes in brain metastable dynamics in preterm, preterm at term-equivalent and full-term neonates. We synthesize findings from EEG, fMRI, fUS, and computational models, focusing on the spatial distribution and temporal dynamics of metastable states, which include functional integration and segregation, signal predictability and complexity. Despite fragmented evidence, studies suggest that neonatal metastability develops over the equivalent of the third gestational trimester, with increasing ability for integration-segregation, broader range of metastable states, faster metastable state transitions and greater signal complexity. Preterms at term-equivalent age exhibit immature metastability features compared to full-terms. We explain and interpret these changes in terms of maturation of the brain in a free energy landscape and establishment of cognitive functions.
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Affiliation(s)
- Juliette L Y Champaud
- Department of Neuroscience, Psychology and Pharmacology, University College London, UK; Centre for the Developing Brain, King's College London, UK
| | - Samanta Asite
- Department of Neuroscience, Psychology and Pharmacology, University College London, UK
| | - Lorenzo Fabrizi
- Department of Neuroscience, Psychology and Pharmacology, University College London, UK.
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2
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Wijaya MT, Mabel-Kenzie STST, Ouyang G, Lee TMC. Metastability in the wild: A scoping review of empirical neuroimaging studies in humans. Neurosci Biobehav Rev 2025; 172:106106. [PMID: 40090532 DOI: 10.1016/j.neubiorev.2025.106106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2024] [Revised: 03/04/2025] [Accepted: 03/11/2025] [Indexed: 03/18/2025]
Abstract
Metastability is proposed as the mechanism supporting our adaptive responses to the environment. While extensive research has characterized brain metastability during rest and task performance, prior studies have mainly focused on understanding underlying mechanisms, with limited exploration of its application in mental processes and behaviors. This scoping review offers an overview of the existing empirical literature in this area. Through a systematic search that included 36 articles, our results reveal a predominance of resting-state fMRI studies, variability in how metastability is defined, and a lack of consideration for common confounds in neuroimaging data. The review concludes with suggestions for future research directions to address crucial unresolved issues in the field.
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Affiliation(s)
- Maria Teresa Wijaya
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong; Laboratory of Neuropsychology & Human Neuroscience, The University of Hong Kong, Hong Kong
| | - Sammi T S T Mabel-Kenzie
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong; Laboratory of Neuropsychology & Human Neuroscience, The University of Hong Kong, Hong Kong
| | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong
| | - Tatia M C Lee
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong; Laboratory of Neuropsychology & Human Neuroscience, The University of Hong Kong, Hong Kong.
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3
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Akella S, Ledochowitsch P, Siegle JH, Belski H, Denman DD, Buice MA, Durand S, Koch C, Olsen SR, Jia X. Deciphering neuronal variability across states reveals dynamic sensory encoding. Nat Commun 2025; 16:1768. [PMID: 39971911 PMCID: PMC11839951 DOI: 10.1038/s41467-025-56733-w] [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: 04/04/2024] [Accepted: 01/29/2025] [Indexed: 02/21/2025] Open
Abstract
Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model conditioned on latent states to partition variability in the mouse visual cortex across internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to local field potentials, we consistently identified three distinct oscillation states, each with a unique variability profile. Regression models within each state revealed a dynamic composition of factors influencing spiking variability, with the dominant factor switching within seconds. The state-conditioned regression model uncovered extensive diversity in source contributions across units, varying in accordance with anatomical hierarchy and internal state. This heterogeneity in encoding underscores the importance of partitioning variability over time, particularly when considering the influence of non-stationary factors on sensory processing.
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Affiliation(s)
| | | | | | | | - Daniel D Denman
- Allen Institute, Seattle, WA, USA
- Anschutz Medical Campus School of Medicine, University of Colorado, Aurora, CO, USA
| | | | | | | | | | - Xiaoxuan Jia
- School of Life Science, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
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4
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Rossi KL, Budzinski RC, Medeiros ES, Boaretto BRR, Muller L, Feudel U. Dynamical properties and mechanisms of metastability: A perspective in neuroscience. Phys Rev E 2025; 111:021001. [PMID: 40103058 DOI: 10.1103/physreve.111.021001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2024] [Indexed: 03/20/2025]
Abstract
Metastability, characterized by a variability of regimes in time, is a ubiquitous type of neural dynamics. It has been formulated in many different ways in the neuroscience literature, however, which may cause some confusion. In this Perspective, we discuss metastability from the point of view of dynamical systems theory. We extract from the literature a very simple but general definition through the concept of metastable regimes as long-lived but transient epochs of activity with unique dynamical properties. This definition serves as an umbrella term that encompasses formulations from other works, and readily connects to concepts from dynamical systems theory. This allows us to examine general dynamical properties of metastable regimes, propose in a didactic manner several dynamics-based mechanisms that generate them, and discuss a theoretical tool to characterize them quantitatively. This Perspective leads to insights that help to address issues debated in the literature and also suggests pathways for future research.
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Affiliation(s)
- Kalel L Rossi
- Carl von Ossietzky University Oldenburg, Theoretical Physics/Complex Systems, ICBM, 26129 Oldenburg, Lower Saxony, Germany
| | - Roberto C Budzinski
- Western University, Department of Mathematics and Western Institute for Neuroscience, N6A 3K7 London, Ontario, Canada
- Fields Institute, Fields Lab for Network Science, M5T 3J1 Toronto, Ontario, Canada
| | - Everton S Medeiros
- São Paulo State University (UNESP), Institute of Geosciences and Exact Sciences, Avenida 24A 1515, 13506-900 Rio Claro, São Paulo, Brazil
| | - Bruno R R Boaretto
- Universidade Federal de São Paulo, Institute of Science and Technology, 12247-014 São José dos Campos, São Paulo, Brazil
- Universitat Politecnica de Catalunya, Department of Physics, 08222 Terrassa, Barcelona, Spain
| | - Lyle Muller
- Western University, Department of Mathematics and Western Institute for Neuroscience, N6A 3K7 London, Ontario, Canada
- Fields Institute, Fields Lab for Network Science, M5T 3J1 Toronto, Ontario, Canada
| | - Ulrike Feudel
- Carl von Ossietzky University Oldenburg, Theoretical Physics/Complex Systems, ICBM, 26129 Oldenburg, Lower Saxony, Germany
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5
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Hancock F, Rosas FE, Luppi AI, Zhang M, Mediano PAM, Cabral J, Deco G, Kringelbach ML, Breakspear M, Kelso JAS, Turkheimer FE. Metastability demystified - the foundational past, the pragmatic present and the promising future. Nat Rev Neurosci 2025; 26:82-100. [PMID: 39663408 DOI: 10.1038/s41583-024-00883-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2024] [Indexed: 12/13/2024]
Abstract
Healthy brain function depends on balancing stable integration between brain areas for effective coordinated functioning, with coexisting segregation that allows subsystems to express their functional specialization. Metastability, a concept from the dynamical systems literature, has been proposed as a key signature that characterizes this balance. Building on this principle, the neuroscience literature has leveraged the phenomenon of metastability to investigate various aspects of brain function in health and disease. However, this body of work often uses the notion of metastability heuristically, and sometimes inaccurately, making it difficult to navigate the vast literature, interpret findings and foster further development of theoretical and experimental methodologies. Here, we provide a comprehensive review of metastability and its applications in neuroscience, covering its scientific and historical foundations and the practical measures used to assess it in empirical data. We also provide a critical analysis of recent theoretical developments, clarifying common misconceptions and paving the road for future developments.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK.
- Sussex Centre for Consciousness Science, University of Sussex, Brighton, UK.
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK.
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
- Sussex AI, University of Sussex, Brighton, UK.
- Centre for Complexity Science, Department of Brain Science, Imperial College London, London, UK.
| | - Andrea I Luppi
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- St John's College, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Mengsen Zhang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK
- Division of Psychology and Language Sciences, University College London, London, UK
| | - Joana Cabral
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Life and Health Sciences Research Institute School of Medicine, University of Minho, Braga, Portugal
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institución Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University Clayton, Melbourne, Victoria, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, New South Wales, Australia
| | - J A Scott Kelso
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland
- The Bath Institute for the Augmented Human, University of Bath, Bath, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- The Institute for Human and Synthetic Minds, King's College London, London, UK
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6
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Li T, La Camera G. A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.08.07.606969. [PMID: 39149270 PMCID: PMC11326216 DOI: 10.1101/2024.08.07.606969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
The application of hidden Markov models (HMMs) to neural data has uncovered hidden states and signatures of neural dynamics that are relevant for sensory and cognitive processes. However, training an HMM on cortical data requires a careful handling of model selection, since models with more numerous hidden states generally have a higher likelihood on new (unseen) data. A potentially related problem is the occurrence of very rapid state switching after decoding the data with an HMM. The first problem can lead to overfitting and over-segmentation of the data. The second problem is due to intermediate-to-low self-transition probabilities and is at odds with many reports that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the 'sticky Poisson-HMM' (sPHMM). When used together with the Bayesian Information Criterion for model selection, the sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. The sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.
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Affiliation(s)
- Tianshu Li
- Department of Neurobiology & Behavior, Stony Brook University
- Graduate Program in Neuroscience, Stony Brook University
- Center for Neural Circuit Dynamics, Stony Brook University
| | - Giancarlo La Camera
- Department of Neurobiology & Behavior, Stony Brook University
- Graduate Program in Neuroscience, Stony Brook University
- Center for Neural Circuit Dynamics, Stony Brook University
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7
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Paliwal S, Ocker GK, Brinkman BAW. Metastability in networks of nonlinear stochastic integrate-and-fire neurons. ARXIV 2024:arXiv:2406.07445v2. [PMID: 38947936 PMCID: PMC11213153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Neurons in the brain continuously process the barrage of sensory inputs they receive from the environment. A wide array of experimental work has shown that the collective activity of neural populations encodes and processes this constant bombardment of information. How these collective patterns of activity depend on single-neuron properties is often unclear. Single-neuron recordings have shown that individual neurons' responses to inputs are nonlinear, which prevents a straight-forward extrapolation from single neuron features to emergent collective states. Here, we use a field-theoretic formulation of a stochastic leaky integrate-and-fire model to study the impact of single-neuron nonlinearities on macroscopic network activity. In this model, a neuron integrates spiking output from other neurons in its membrane voltage and emits spikes stochastically with an intensity depending on the membrane voltage, after which the voltage resets. We show that the interplay between nonlinear spike intensity functions and membrane potential resets can i) give rise to metastable active firing rate states in recurrent networks, and ii) can enhance or suppress mean firing rates and membrane potentials in the same or paradoxically opposite directions.
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Affiliation(s)
- Siddharth Paliwal
- Graduate Program in Neuroscience, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Gabriel Koch Ocker
- Department of Mathematics and Statistics, Boston University, Boston, MA, 02215, USA
| | - Braden A. W. Brinkman
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA
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8
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Pavlidis E, Campillo F, Goldbeter A, Desroches M. Multiple-timescale dynamics, mixed mode oscillations and mixed affective states in a model of bipolar disorder. Cogn Neurodyn 2024; 18:3239-3257. [PMID: 39712089 PMCID: PMC11655942 DOI: 10.1007/s11571-022-09900-4] [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: 04/01/2022] [Revised: 09/03/2022] [Accepted: 10/09/2022] [Indexed: 11/28/2022] Open
Abstract
Mixed affective states in bipolar disorder (BD) is a common psychiatric condition that occurs when symptoms of the two opposite poles coexist during an episode of mania or depression. A four-dimensional model by Goldbeter (Progr Biophys Mol Biol 105:119-127, 2011; Pharmacopsychiatry 46:S44-S52, 2013) rests upon the notion that manic and depressive symptoms are produced by two competing and auto-inhibited neural networks. Some of the rich dynamics that this model can produce, include complex rhythms formed by both small-amplitude (subthreshold) and large-amplitude (suprathreshold) oscillations and could correspond to mixed bipolar states. These rhythms are commonly referred to as mixed mode oscillations (MMOs) and they have already been studied in many different contexts by Bertram (Mathematical analysis of complex cellular activity, Springer, Cham, 2015), (Petrov et al. in J Chem Phys 97:6191-6198, 1992). In order to accurately explain these dynamics one has to apply a mathematical apparatus that makes full use of the timescale separation between variables. Here we apply the framework of multiple-timescale dynamics to the model of BD in order to understand the mathematical mechanisms underpinning the observed dynamics of changing mood. We show that the observed complex oscillations can be understood as MMOs due to a so-called folded-node singularity. Moreover, we explore the bifurcation structure of the system and we provide possible biological interpretations of our findings. Finally, we show the robustness of the MMOs regime to stochastic noise and we propose a minimal three-dimensional model which, with the addition of noise, exhibits similar yet purely noise-driven dynamics. The broader significance of this work is to introduce mathematical tools that could be used to analyse and potentially control future, more biologically grounded models of BD.
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Affiliation(s)
- Efstathios Pavlidis
- Neuromod Institute, Université Côte d’Azur, 2004 route des Lucioles-BP93, Sophia Antipolis, 06902 France
- MathNeuro Team, Inria at Université Côte d’Azur, 2004 route des Lucioles-BP93, Sophia Antipolis, 06902 France
| | - Fabien Campillo
- MathNeuro Team, Inria at Université Côte d’Azur, 2004 route des Lucioles-BP93, Sophia Antipolis, 06902 France
| | - Albert Goldbeter
- Unité de Chronobiologie théorique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Campus Plaine, CP 231, Brussels, B-1050 Belgium
| | - Mathieu Desroches
- MathNeuro Team, Inria at Université Côte d’Azur, 2004 route des Lucioles-BP93, Sophia Antipolis, 06902 France
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9
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Cihak HL, Kilpatrick ZP. Robustly encoding certainty in a metastable neural circuit model. Phys Rev E 2024; 110:034404. [PMID: 39425424 PMCID: PMC11778249 DOI: 10.1103/physreve.110.034404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 08/19/2024] [Indexed: 10/21/2024]
Abstract
Localized persistent neural activity can encode delayed estimates of continuous variables. Common experiments require that subjects store and report the feature value (e.g., orientation) of a particular cue (e.g., oriented bar on a screen) after a delay. Visualizing recorded activity of neurons along their feature tuning reveals activity bumps whose centers wander stochastically, degrading the estimate over time. Bump position therefore represents the remembered estimate. Recent work suggests bump amplitude may represent estimate certainty reflecting a probabilistic population code for a Bayesian posterior. Idealized models of this type are fragile due to the fine tuning common to constructed continuum attractors in dynamical systems. Here we propose an alternative metastable model for robustly supporting multiple bump amplitudes by extending neural circuit models to include quantized nonlinearities. Asymptotic projections of circuit activity produce low-dimensional evolution equations for the amplitude and position of bump solutions in response to external stimuli and noise perturbations. Analysis of reduced equations accurately characterizes phase variance and the dynamics of amplitude transitions between stable discrete values. More salient cues generate bumps of higher amplitude which wander less, consistent with experiments showing certainty correlates with more accurate memories.
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Affiliation(s)
- Heather L. Cihak
- Department of Applied Mathematics, University of Colorado, Boulder, Colorado 80309, USA
| | - Zachary P. Kilpatrick
- Department of Applied Mathematics, University of Colorado, Boulder, Colorado 80309, USA
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10
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Rostami V, Rost T, Schmitt FJ, van Albada SJ, Riehle A, Nawrot MP. Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information. Nat Commun 2024; 15:6304. [PMID: 39060243 PMCID: PMC11282312 DOI: 10.1038/s41467-024-49889-4] [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: 03/29/2022] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
When preparing a movement, we often rely on partial or incomplete information, which can decrement task performance. In behaving monkeys we show that the degree of cued target information is reflected in both, neural variability in motor cortex and behavioral reaction times. We study the underlying mechanisms in a spiking motor-cortical attractor model. By introducing a biologically realistic network topology where excitatory neuron clusters are locally balanced with inhibitory neuron clusters we robustly achieve metastable network activity across a wide range of network parameters. In application to the monkey task, the model performs target-specific action selection and accurately reproduces the task-epoch dependent reduction of trial-to-trial variability in vivo where the degree of reduction directly reflects the amount of processed target information, while spiking irregularity remained constant throughout the task. In the context of incomplete cue information, the increased target selection time of the model can explain increased behavioral reaction times. We conclude that context-dependent neural and behavioral variability is a signum of attractor computation in the motor cortex.
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Affiliation(s)
- Vahid Rostami
- Institute of Zoology, University of Cologne, Cologne, Germany
| | - Thomas Rost
- Institute of Zoology, University of Cologne, Cologne, Germany
| | | | - Sacha Jennifer van Albada
- Institute of Zoology, University of Cologne, Cologne, Germany
- Institute for Advanced Simulation (IAS-6), Jülich Research Center, Jülich, Germany
| | - Alexa Riehle
- Institute for Advanced Simulation (IAS-6), Jülich Research Center, Jülich, Germany
- UMR7289 Institut de Neurosciences de la Timone (INT), Centre National de la Recherche Scientifique (CNRS)-Aix-Marseille Université (AMU), Marseille, France
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11
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Yang X, La Camera G. Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits. PLoS Comput Biol 2024; 20:e1012220. [PMID: 38950068 PMCID: PMC11244818 DOI: 10.1371/journal.pcbi.1012220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 07/12/2024] [Accepted: 06/01/2024] [Indexed: 07/03/2024] Open
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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Affiliation(s)
- Xiaoyu Yang
- Graduate Program in Physics and Astronomy, Stony Brook University, Stony Brook, New York, United States of America
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
- Center for Neural Circuit Dynamics, Stony Brook University, Stony Brook, New York, United States of America
| | - Giancarlo La Camera
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
- Center for Neural Circuit Dynamics, Stony Brook University, Stony Brook, New York, United States of America
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12
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Tsubo Y, Shinomoto S. Nondifferentiable activity in the brain. PNAS NEXUS 2024; 3:pgae261. [PMID: 38994500 PMCID: PMC11238849 DOI: 10.1093/pnasnexus/pgae261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/05/2024] [Indexed: 07/13/2024]
Abstract
Spike raster plots of numerous neurons show vertical stripes, indicating that neurons exhibit synchronous activity in the brain. We seek to determine whether these coherent dynamics are caused by smooth brainwave activity or by something else. By analyzing biological data, we find that their cross-correlograms exhibit not only slow undulation but also a cusp at the origin, in addition to possible signs of monosynaptic connectivity. Here we show that undulation emerges if neurons are subject to smooth brainwave oscillations while a cusp results from nondifferentiable fluctuations. While modern analysis methods have achieved good connectivity estimation by adapting the models to slow undulation, they still make false inferences due to the cusp. We devise a new analysis method that may solve both problems. We also demonstrate that oscillations and nondifferentiable fluctuations may emerge in simulations of large-scale neural networks.
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Affiliation(s)
- Yasuhiro Tsubo
- College of Information Science and Engineering, Ritsumeikan University, Osaka 567-8570, Japan
| | - Shigeru Shinomoto
- Research Organization of Open Innovation and Collaboration, Ritsumeikan University, Osaka 567-8570, Japan
- Graduate School of Biostudies, Kyoto University, Kyoto 606-8501, Japan
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13
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Yang X, La Camera G. 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] [Grants] [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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Affiliation(s)
- Xiaoyu Yang
- Graduate Program in Physics and Astronomy, Stony Brook University
- Department of Neurobiology & Behavior, Stony Brook University
- Center for Neural Circuit Dynamics, Stony Brook University
| | - Giancarlo La Camera
- Department of Neurobiology & Behavior, Stony Brook University
- Center for Neural Circuit Dynamics, Stony Brook University
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14
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Svedberg DA, Katz DB. Neural correlates of rapid familiarization to novel taste. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593234. [PMID: 38766243 PMCID: PMC11100709 DOI: 10.1101/2024.05.08.593234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The gustatory cortex (GC) plays a pivotal role in taste perception, with neural ensemble responses reflecting taste quality and influencing behavior. Recent work, however, has shown that GC taste responses change across sessions of novel taste exposure in taste-naïve rats. Here, we use single-trial analyses to explore changes in the cortical taste-code on the scale of individual trials. Contrary to the traditional view of taste perception as innate, our findings suggest rapid, experience-dependent changes in GC responses during initial taste exposure trials. Specifically, we find that early responses to novel taste are less "stereotyped" and encode taste identity less reliably compared to later responses. These changes underscore the dynamic nature of sensory processing and provides novel insights into the real-time dynamics of sensory processing across novel-taste familiarization.
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15
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Kogan JF, Fontanini A. Learning enhances representations of taste-guided decisions in the mouse gustatory insular cortex. Curr Biol 2024; 34:1880-1892.e5. [PMID: 38631343 PMCID: PMC11188718 DOI: 10.1016/j.cub.2024.03.034] [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: 10/16/2023] [Revised: 02/07/2024] [Accepted: 03/19/2024] [Indexed: 04/19/2024]
Abstract
Learning to discriminate overlapping gustatory stimuli that predict distinct outcomes-a feat known as discrimination learning-can mean the difference between ingesting a poison or a nutritive meal. Despite the obvious importance of this process, very little is known about the neural basis of taste discrimination learning. In other sensory modalities, this form of learning can be mediated by either the sharpening of sensory representations or the enhanced ability of "decision-making" circuits to interpret sensory information. Given the dual role of the gustatory insular cortex (GC) in encoding both sensory and decision-related variables, this region represents an ideal site for investigating how neural activity changes as animals learn a novel taste discrimination. Here, we present results from experiments relying on two-photon calcium imaging of GC neural activity in mice performing a taste-guided mixture discrimination task. The task allows for the recording of neural activity before and after learning induced by training mice to discriminate increasingly similar pairs of taste mixtures. Single-neuron and population analyses show a time-varying pattern of activity, with early sensory responses emerging after taste delivery and binary, choice-encoding responses emerging later in the delay before a decision is made. Our results demonstrate that, while both sensory and decision-related information is encoded by GC in the context of a taste mixture discrimination task, learning and improved performance are associated with a specific enhancement of decision-related responses.
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Affiliation(s)
- Joshua F Kogan
- Graduate Program in Neuroscience, Stony Brook University, Stony Brook, NY 11794, USA; Medical Scientist Training Program, Stony Brook University, Stony Brook, NY 11794, USA; Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA.
| | - Alfredo Fontanini
- Graduate Program in Neuroscience, Stony Brook University, Stony Brook, NY 11794, USA; Medical Scientist Training Program, Stony Brook University, Stony Brook, NY 11794, USA; Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA.
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16
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Papadopoulos L, Jo S, Zumwalt K, Wehr M, McCormick DA, Mazzucato L. Modulation of metastable ensemble dynamics explains optimal coding at moderate arousal in auditory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.04.588209. [PMID: 38617286 PMCID: PMC11014582 DOI: 10.1101/2024.04.04.588209] [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
Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture. Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal). Additional signatures of this transition include arousal-induced reductions of overall neural variability and the extent of stimulus-induced variability quenching, which we observed in the empirical data. Altogether, this study elucidates computational principles underlying interactions between pupil-linked arousal, sensory processing, and neural variability, and suggests a role for phase transitions in explaining nonlinear modulations of cortical computations.
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Affiliation(s)
| | - Suhyun Jo
- Institute of Neuroscience, University of Oregon, Eugene, Oregon
| | - Kevin Zumwalt
- Institute of Neuroscience, University of Oregon, Eugene, Oregon
| | - Michael Wehr
- Institute of Neuroscience, University of Oregon, Eugene, Oregon and Department of Psychology, University of Oregon, Eugene, Oregon
| | - David A McCormick
- Institute of Neuroscience, University of Oregon, Eugene, Oregon and Department of Biology, University of Oregon, Eugene, Oregon
| | - Luca Mazzucato
- Institute of Neuroscience, University of Oregon, Eugene, Oregon
- Department of Biology, University of Oregon, Eugene, Oregon
- Department of Mathematics, University of Oregon, Eugene, Oregon and Department of Physics, University of Oregon, Eugene, Oregon
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17
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Breffle J, Mokashe S, Qiu S, Miller P. Multistability in neural systems with random cross-connections. BIOLOGICAL CYBERNETICS 2023; 117:485-506. [PMID: 38133664 PMCID: PMC11773687 DOI: 10.1007/s00422-023-00981-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023]
Abstract
Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems using a firing rate model framework, in which clusters of similarly responsive neurons are represented as single units, which interact with each other through independent random connections. We explore the range of conditions in which multistability arises via recurrent input from other units while individual units, typically with some degree of self-excitation, lack sufficient self-excitation to become bistable on their own. We find many cases of multistability-defined as the system possessing more than one stable fixed point-in which stable states arise via a network effect, allowing subsets of units to maintain each others' activity because their net input to each other when active is sufficiently positive. In terms of the strength of within-unit self-excitation and standard deviation of random cross-connections, the region of multistability depends on the response function of units. Indeed, multistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the response function rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which produces Zipf's Law when enumerating the proportion of trials within which random initial conditions lead to a particular stable state of the system.
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Affiliation(s)
- Jordan Breffle
- Neuroscience Program, Brandeis University, 415 South St, Waltham, MA, 02454, USA
| | - Subhadra Mokashe
- Neuroscience Program, Brandeis University, 415 South St, Waltham, MA, 02454, USA
| | - Siwei Qiu
- Volen National Center for Complex Systems, Brandeis University, 415 South St, Waltham, MA, 02454, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Miller
- Neuroscience Program, Brandeis University, 415 South St, Waltham, MA, 02454, USA.
- Volen National Center for Complex Systems, Brandeis University, 415 South St, Waltham, MA, 02454, USA.
- Department of Biology, Brandeis University, 415 South St, Waltham, MA, 02454, USA.
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18
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Kogan JF, Fontanini A. Learning enhances representations of taste-guided decisions in the mouse gustatory insular cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.16.562605. [PMID: 37905010 PMCID: PMC10614904 DOI: 10.1101/2023.10.16.562605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Learning to discriminate overlapping gustatory stimuli that predict distinct outcomes - a feat known as discrimination learning - can mean the difference between ingesting a poison or a nutritive meal. Despite the obvious importance of this process, very little is known on the neural basis of taste discrimination learning. In other sensory modalities, this form of learning can be mediated by either sharpening of sensory representations, or enhanced ability of "decision-making" circuits to interpret sensory information. Given the dual role of the gustatory insular cortex (GC) in encoding both sensory and decision-related variables, this region represents an ideal site for investigating how neural activity changes as animals learn a novel taste discrimination. Here we present results from experiments relying on two photon calcium imaging of GC neural activity in mice performing a taste-guided mixture discrimination task. The task allows for recording of neural activity before and after learning induced by training mice to discriminate increasingly similar pairs of taste mixtures. Single neuron and population analyses show a time-varying pattern of activity, with early sensory responses emerging after taste delivery and binary, choice encoding responses emerging later in the delay before a decision is made. Our results demonstrate that while both sensory and decision-related information is encoded by GC in the context of a taste mixture discrimination task, learning and improved performance are associated with a specific enhancement of decision-related responses.
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19
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Dallmer-Zerbe I, Jajcay N, Chvojka J, Janca R, Jezdik P, Krsek P, Marusic P, Jiruska P, Hlinka J. Computational modeling allows unsupervised classification of epileptic brain states across species. Sci Rep 2023; 13:13436. [PMID: 37596382 PMCID: PMC10439162 DOI: 10.1038/s41598-023-39867-z] [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: 04/17/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023] Open
Abstract
Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Nikola Jajcay
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- National Institute of Mental Health, 250 67, Klecany, Czech Republic
| | - Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Radek Janca
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Petr Jezdik
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Pavel Krsek
- Department of Paediatric Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic.
- National Institute of Mental Health, 250 67, Klecany, Czech Republic.
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20
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Breffle J, Mokashe S, Qiu S, Miller P. Multistability in neural systems with random cross-connections. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543727. [PMID: 37333310 PMCID: PMC10274702 DOI: 10.1101/2023.06.05.543727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems, using a firing-rate model framework, in which clusters of neurons with net self-excitation are represented as units, which interact with each other through random connections. We focus on conditions in which individual units lack sufficient self-excitation to become bistable on their own. Rather, multistability can arise via recurrent input from other units as a network effect for subsets of units, whose net input to each other when active is sufficiently positive to maintain such activity. In terms of the strength of within-unit self-excitation and standard-deviation of random cross-connections, the region of multistability depends on the firing-rate curve of units. Indeed, bistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the firing-rate curve rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which can appear as Zipf's Law when sampled as the proportion of trials within which random initial conditions lead to a particular stable state of the system.
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Affiliation(s)
- Jordan Breffle
- Neuroscience Program, Brandeis University, 415 South St, Waltham, MA 02454
| | - Subhadra Mokashe
- Neuroscience Program, Brandeis University, 415 South St, Waltham, MA 02454
| | - Siwei Qiu
- Volen National Center for Complex Systems, Brandeis University, 415 South St, Waltham, MA 02454
- Current address: Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Miller
- Neuroscience Program, Brandeis University, 415 South St, Waltham, MA 02454
- Volen National Center for Complex Systems, Brandeis University, 415 South St, Waltham, MA 02454
- Department of Biology, Brandeis University, 415 South St, Waltham, MA 02454
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21
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Temporal progression along discrete coding states during decision-making in the mouse gustatory cortex. PLoS Comput Biol 2023; 19:e1010865. [PMID: 36749734 PMCID: PMC9904478 DOI: 10.1371/journal.pcbi.1010865] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/10/2023] [Indexed: 02/08/2023] Open
Abstract
The mouse gustatory cortex (GC) is involved in taste-guided decision-making in addition to sensory processing. Rodent GC exhibits metastable neural dynamics during ongoing and stimulus-evoked activity, but how these dynamics evolve in the context of a taste-based decision-making task remains unclear. Here we employ analytical and modeling approaches to i) extract metastable dynamics in ensemble spiking activity recorded from the GC of mice performing a perceptual decision-making task; ii) investigate the computational mechanisms underlying GC metastability in this task; and iii) establish a relationship between GC dynamics and behavioral performance. Our results show that activity in GC during perceptual decision-making is metastable and that this metastability may serve as a substrate for sequentially encoding sensory, abstract cue, and decision information over time. Perturbations of the model's metastable dynamics indicate that boosting inhibition in different coding epochs differentially impacts network performance, explaining a counterintuitive effect of GC optogenetic silencing on mouse behavior.
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22
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Mahmood A, Steindler J, Germaine H, Miller P, Katz DB. Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity. J Neurosci 2023; 43:386-404. [PMID: 36443002 PMCID: PMC9864615 DOI: 10.1523/jneurosci.1412-22.2022] [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: 07/20/2022] [Revised: 11/12/2022] [Accepted: 11/15/2022] [Indexed: 11/29/2022] Open
Abstract
Gustatory cortical (GC) single-neuron taste responses reflect taste quality and palatability in successive epochs. Ensemble analyses reveal epoch-to-epoch firing-rate changes in these responses to be sudden, coherent transitions. Such nonlinear dynamics suggest that GC is part of a recurrent network, producing these dynamics in concert with other structures. Basolateral amygdala (BLA), which is reciprocally connected to GC and central to hedonic processing, is a strong candidate partner for GC, in that BLA taste responses evolve on the same general clock as GC and because inhibition of activity in the BLA→GC pathway degrades the sharpness of GC transitions. These facts motivate, but do not test, our overarching hypothesis that BLA and GC act as a single, comodulated network during taste processing. Here, we provide just this test of simultaneous (BLA and GC) extracellular taste responses in female rats, probing the multiregional dynamics of activity to directly test whether BLA and GC responses contain coupled dynamics. We show that BLA and GC response magnitudes covary across trials and within single responses, and that changes in BLA-GC local field potential phase coherence are epoch specific. Such classic coherence analyses, however, obscure the most salient facet of BLA-GC coupling: sudden transitions in and out of the epoch known to be involved in driving gaping behavior happen near simultaneously in the two regions, despite huge trial-to-trial variability in transition latencies. This novel form of inter-regional coupling, which we show is easily replicated in model networks, suggests collective processing in a distributed neural network.SIGNIFICANCE STATEMENT There has been little investigation into real-time communication between brain regions during taste processing, a fact reflecting the dominant belief that taste circuitry is largely feedforward. Here, we perform an in-depth analysis of real-time interactions between GC and BLA in response to passive taste deliveries, using both conventional coherence metrics and a novel methodology that explicitly considers trial-to-trial variability and fast single-trial dynamics in evoked responses. Our results demonstrate that BLA-GC coherence changes as the taste response unfolds, and that BLA and GC specifically couple for the sudden transition into (and out of) the behaviorally relevant neural response epoch, suggesting (although not proving) that: (1) recurrent interactions subserve the function of the dyad as (2) a putative attractor network.
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Affiliation(s)
- Abuzar Mahmood
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts 02453
| | | | - Hannah Germaine
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts 02453
| | - Paul Miller
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts 02453
- Biology, Brandeis University, Waltham, Massachusetts 02453
- Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02453
| | - Donald B Katz
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts 02453
- Departments of Psychology
- Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02453
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23
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Scott DN, Frank MJ. Adaptive control of synaptic plasticity integrates micro- and macroscopic network function. Neuropsychopharmacology 2023; 48:121-144. [PMID: 36038780 PMCID: PMC9700774 DOI: 10.1038/s41386-022-01374-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/09/2022]
Abstract
Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.
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Affiliation(s)
- Daniel N Scott
- Cognitive Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| | - Michael J Frank
- Cognitive Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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24
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Monaco JD, Hwang GM. Neurodynamical Computing at the Information Boundaries of Intelligent Systems. Cognit Comput 2022; 16:1-13. [PMID: 39129840 PMCID: PMC11306504 DOI: 10.1007/s12559-022-10081-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/15/2022] [Indexed: 12/28/2022]
Abstract
Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies-properly conceived as reentrant dynamical flows and not merely as identified groups of neurons-may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.
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Affiliation(s)
- Joseph D. Monaco
- Dept of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Grace M. Hwang
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD USA
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25
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Pietras B, Schmutz V, Schwalger T. Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. PLoS Comput Biol 2022; 18:e1010809. [PMID: 36548392 PMCID: PMC9822116 DOI: 10.1371/journal.pcbi.1010809] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 01/06/2023] [Accepted: 12/11/2022] [Indexed: 12/24/2022] Open
Abstract
Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.
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Affiliation(s)
- Bastian Pietras
- Institute for Mathematics, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Valentin Schmutz
- Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tilo Schwalger
- Institute for Mathematics, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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26
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Miehl C, Onasch S, Festa D, Gjorgjieva J. Formation and computational implications of assemblies in neural circuits. J Physiol 2022. [PMID: 36068723 DOI: 10.1113/jp282750] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/22/2022] [Indexed: 11/08/2022] Open
Abstract
In the brain, patterns of neural activity represent sensory information and store it in non-random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain. Abstract figure legend Assembly Formation. Assemblies are groups of strongly connected neurons formed by the interaction of multiple mechanisms and with vast computational implications. Four interacting components are thought to drive assembly formation: synaptic plasticity, symmetry breaking, competition and stability. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Christoph Miehl
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Sebastian Onasch
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Dylan Festa
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Julijana Gjorgjieva
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
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Mazzucato L. Neural mechanisms underlying the temporal organization of naturalistic animal behavior. eLife 2022; 11:e76577. [PMID: 35792884 PMCID: PMC9259028 DOI: 10.7554/elife.76577] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/07/2022] [Indexed: 12/17/2022] Open
Abstract
Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
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Affiliation(s)
- Luca Mazzucato
- Institute of Neuroscience, Departments of Biology, Mathematics and Physics, University of OregonEugeneUnited States
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28
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Neural Mechanisms of the Maintenance and Manipulation of Gustatory Working Memory in Orbitofrontal Cortex. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10035-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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29
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Fontanier V, Sarazin M, Stoll FM, Delord B, Procyk E. Inhibitory control of frontal metastability sets the temporal signature of cognition. eLife 2022; 11:63795. [PMID: 35635439 PMCID: PMC9200403 DOI: 10.7554/elife.63795] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.
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Affiliation(s)
| | - Matthieu Sarazin
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Frederic M Stoll
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Bruno Delord
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Emmanuel Procyk
- Stem Cell and Brain Research Institute U1208, Inserm, Lyon, France
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30
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Howland JG, Ito R, Lapish CC, Villaruel FR. The rodent medial prefrontal cortex and associated circuits in orchestrating adaptive behavior under variable demands. Neurosci Biobehav Rev 2022; 135:104569. [PMID: 35131398 PMCID: PMC9248379 DOI: 10.1016/j.neubiorev.2022.104569] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 12/17/2021] [Accepted: 02/01/2022] [Indexed: 11/28/2022]
Abstract
Emerging evidence implicates rodent medial prefrontal cortex (mPFC) in tasks requiring adaptation of behavior to changing information from external and internal sources. However, the computations within mPFC and subsequent outputs that determine behavior are incompletely understood. We review the involvement of mPFC subregions, and their projections to the striatum and amygdala in two broad types of tasks in rodents: 1) appetitive and aversive Pavlovian and operant conditioning tasks that engage mPFC-striatum and mPFC-amygdala circuits, and 2) foraging-based tasks that require decision making to optimize reward. We find support for region-specific function of the mPFC, with dorsal mPFC and its projections to the dorsomedial striatum supporting action control with higher cognitive demands, and ventral mPFC engagement in translating affective signals into behavior via discrete projections to the ventral striatum and amygdala. However, we also propose that defined mPFC subdivisions operate as a functional continuum rather than segregated functional units, with crosstalk that allows distinct subregion-specific inputs (e.g., internal, affective) to influence adaptive behavior supported by other subregions.
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Affiliation(s)
- John G Howland
- Department of Anatomy, Physiology, and Pharmacology, University of Saskatchewan, Saskatoon, SK, Canada.
| | - Rutsuko Ito
- Department of Psychology, University of Toronto-Scarborough, Toronto, ON, Canada.
| | - Christopher C Lapish
- Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.
| | - Franz R Villaruel
- Department of Psychology, Concordia University, Montreal, QC, Canada.
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31
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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: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [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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32
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Metastable attractors explain the variable timing of stable behavioral action sequences. Neuron 2022; 110:139-153.e9. [PMID: 34717794 PMCID: PMC9194601 DOI: 10.1016/j.neuron.2021.10.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/30/2021] [Accepted: 10/05/2021] [Indexed: 01/07/2023]
Abstract
The timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same neural circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action-timing fluctuations. Using hidden Markov models, we established a dictionary between activity patterns and actions. We then showed that metastable attractors, representing activity patterns with a reliable sequential structure and large transition timing variability, could be produced by reciprocally coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors relied on correlated variability in this mesoscale feedback loop, predicting a specific structure of low-dimensional correlations that were empirically verified in M2 recordings. Our results suggest a novel mesoscale network motif based on correlated variability supporting naturalistic animal behavior.
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33
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The Mean Field Approach for Populations of Spiking Neurons. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:125-157. [DOI: 10.1007/978-3-030-89439-9_6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractMean field theory is a device to analyze the collective behavior of a dynamical system comprising many interacting particles. The theory allows to reduce the behavior of the system to the properties of a handful of parameters. In neural circuits, these parameters are typically the firing rates of distinct, homogeneous subgroups of neurons. Knowledge of the firing rates under conditions of interest can reveal essential information on both the dynamics of neural circuits and the way they can subserve brain function. The goal of this chapter is to provide an elementary introduction to the mean field approach for populations of spiking neurons. We introduce the general idea in networks of binary neurons, starting from the most basic results and then generalizing to more relevant situations. This allows to derive the mean field equations in a simplified setting. We then derive the mean field equations for populations of integrate-and-fire neurons. An effort is made to derive the main equations of the theory using only elementary methods from calculus and probability theory. The chapter ends with a discussion of the assumptions of the theory and some of the consequences of violating those assumptions. This discussion includes an introduction to balanced and metastable networks and a brief catalogue of successful applications of the mean field approach to the study of neural circuits.
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34
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Huang C. Modulation of the dynamical state in cortical network models. Curr Opin Neurobiol 2021; 70:43-50. [PMID: 34403890 PMCID: PMC8688204 DOI: 10.1016/j.conb.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/18/2021] [Accepted: 07/14/2021] [Indexed: 11/29/2022]
Abstract
Cortical neural responses can be modulated by various factors, such as stimulus inputs and the behavior state of the animal. Understanding the circuit mechanisms underlying modulations of network dynamics is important to understand the flexibility of circuit computations. Identifying the dynamical state of a network is an important first step to predict network responses to external stimulus and top-down modulatory inputs. Models in stable or unstable dynamical regimes require different analytic tools to estimate the network responses to inputs and the structure of neural variability. In this article, I review recent cortical models of state-dependent responses and their predictions about the underlying modulatory mechanisms.
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Affiliation(s)
- Chengcheng Huang
- Departments of Neuroscience and Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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35
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Cao R, Pastukhov A, Aleshin S, Mattia M, Braun J. Binocular rivalry reveals an out-of-equilibrium neural dynamics suited for decision-making. eLife 2021; 10:e61581. [PMID: 34369875 PMCID: PMC8352598 DOI: 10.7554/elife.61581] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 05/24/2021] [Indexed: 12/19/2022] Open
Abstract
In ambiguous or conflicting sensory situations, perception is often 'multistable' in that it perpetually changes at irregular intervals, shifting abruptly between distinct alternatives. The interval statistics of these alternations exhibits quasi-universal characteristics, suggesting a general mechanism. Using binocular rivalry, we show that many aspects of this perceptual dynamics are reproduced by a hierarchical model operating out of equilibrium. The constitutive elements of this model idealize the metastability of cortical networks. Independent elements accumulate visual evidence at one level, while groups of coupled elements compete for dominance at another level. As soon as one group dominates perception, feedback inhibition suppresses supporting evidence. Previously unreported features in the serial dependencies of perceptual alternations compellingly corroborate this mechanism. Moreover, the proposed out-of-equilibrium dynamics satisfies normative constraints of continuous decision-making. Thus, multistable perception may reflect decision-making in a volatile world: integrating evidence over space and time, choosing categorically between hypotheses, while concurrently evaluating alternatives.
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Affiliation(s)
- Robin Cao
- Cognitive Biology, Center for Behavioral Brain SciencesMagdeburgGermany
- Gatsby Computational Neuroscience UnitLondonUnited Kingdom
- Istituto Superiore di SanitàRomeItaly
| | | | - Stepan Aleshin
- Cognitive Biology, Center for Behavioral Brain SciencesMagdeburgGermany
| | | | - Jochen Braun
- Cognitive Biology, Center for Behavioral Brain SciencesMagdeburgGermany
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36
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Sarazin MXB, Victor J, Medernach D, Naudé J, Delord B. Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the Irregular Asynchronous State. Front Neural Circuits 2021; 15:648538. [PMID: 34305535 PMCID: PMC8298038 DOI: 10.3389/fncir.2021.648538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/31/2021] [Indexed: 11/13/2022] Open
Abstract
In the prefrontal cortex (PFC), higher-order cognitive functions and adaptive flexible behaviors rely on continuous dynamical sequences of spiking activity that constitute neural trajectories in the state space of activity. Neural trajectories subserve diverse representations, from explicit mappings in physical spaces to generalized mappings in the task space, and up to complex abstract transformations such as working memory, decision-making and behavioral planning. Computational models have separately assessed learning and replay of neural trajectories, often using unrealistic learning rules or decoupling simulations for learning from replay. Hence, the question remains open of how neural trajectories are learned, memorized and replayed online, with permanently acting biological plasticity rules. The asynchronous irregular regime characterizing cortical dynamics in awake conditions exerts a major source of disorder that may jeopardize plasticity and replay of locally ordered activity. Here, we show that a recurrent model of local PFC circuitry endowed with realistic synaptic spike timing-dependent plasticity and scaling processes can learn, memorize and replay large-size neural trajectories online under asynchronous irregular dynamics, at regular or fast (sped-up) timescale. Presented trajectories are quickly learned (within seconds) as synaptic engrams in the network, and the model is able to chunk overlapping trajectories presented separately. These trajectory engrams last long-term (dozen hours) and trajectory replays can be triggered over an hour. In turn, we show the conditions under which trajectory engrams and replays preserve asynchronous irregular dynamics in the network. Functionally, spiking activity during trajectory replays at regular timescale accounts for both dynamical coding with temporal tuning in individual neurons, persistent activity at the population level, and large levels of variability consistent with observed cognitive-related PFC dynamics. Together, these results offer a consistent theoretical framework accounting for how neural trajectories can be learned, memorized and replayed in PFC networks circuits to subserve flexible dynamic representations and adaptive behaviors.
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Affiliation(s)
- Matthieu X B Sarazin
- Institut des Systèmes Intelligents et de Robotique, CNRS, Inserm, Sorbonne Université, Paris, France
| | - Julie Victor
- CEA Paris-Saclay, CNRS, NeuroSpin, Saclay, France
| | - David Medernach
- Institut des Systèmes Intelligents et de Robotique, CNRS, Inserm, Sorbonne Université, Paris, France
| | - Jérémie Naudé
- Neuroscience Paris Seine - Institut de biologie Paris Seine, CNRS, Inserm, Sorbonne Université, Paris, France
| | - Bruno Delord
- Institut des Systèmes Intelligents et de Robotique, CNRS, Inserm, Sorbonne Université, Paris, France
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37
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Lin JY, Mukherjee N, Bernstein MJ, Katz DB. Perturbation of amygdala-cortical projections reduces ensemble coherence of palatability coding in gustatory cortex. eLife 2021; 10:e65766. [PMID: 34018924 PMCID: PMC8139825 DOI: 10.7554/elife.65766] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/30/2021] [Indexed: 01/01/2023] Open
Abstract
Taste palatability is centrally involved in consumption decisions-we ingest foods that taste good and reject those that don't. Gustatory cortex (GC) and basolateral amygdala (BLA) almost certainly work together to mediate palatability-driven behavior, but the precise nature of their interplay during taste decision-making is still unknown. To probe this issue, we discretely perturbed (with optogenetics) activity in rats' BLA→GC axons during taste deliveries. This perturbation strongly altered GC taste responses, but while the perturbation itself was tonic (2.5 s), the alterations were not-changes preferentially aligned with the onset times of previously-described taste response epochs, and reduced evidence of palatability-related activity in the 'late-epoch' of the responses without reducing the amount of taste identity information available in the 'middle epoch.' Finally, BLA→GC perturbations changed behavior-linked taste response dynamics themselves, distinctively diminishing the abruptness of ensemble transitions into the late epoch. These results suggest that BLA 'organizes' behavior-related GC taste dynamics.
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Affiliation(s)
- Jian-You Lin
- Department of PsychologyWalthamUnited States
- The Volen National Center for Complex Systems, Brandeis UniversityWalthamUnited States
| | - Narendra Mukherjee
- The Volen National Center for Complex Systems, Brandeis UniversityWalthamUnited States
| | - Max J Bernstein
- Department of PsychologyWalthamUnited States
- The Volen National Center for Complex Systems, Brandeis UniversityWalthamUnited States
| | - Donald B Katz
- Department of PsychologyWalthamUnited States
- The Volen National Center for Complex Systems, Brandeis UniversityWalthamUnited States
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38
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Benozzo D, La Camera G, Genovesio A. Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task. Cell Rep 2021; 35:108934. [PMID: 33826896 PMCID: PMC8083966 DOI: 10.1016/j.celrep.2021.108934] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 01/10/2021] [Accepted: 03/11/2021] [Indexed: 11/20/2022] Open
Abstract
Cortical activity related to erroneous behavior in discrimination or decision-making tasks is rarely analyzed, yet it can help clarify which computations are essential during a specific task. Here, we use a hidden Markov model (HMM) to perform a trial-by-trial analysis of the ensemble activity of dorsolateral prefrontal cortex (PFdl) neurons of rhesus monkeys performing a distance discrimination task. By segmenting the neural activity into sequences of metastable states, HMM allows us to uncover modulations of the neural dynamics related to internal computations. We find that metastable dynamics slow down during error trials, while state transitions at a pivotal point during the trial take longer in difficult correct trials. Both these phenomena occur during the decision interval, with errors occurring in both easy and difficult trials. Our results provide further support for the emerging role of metastable cortical dynamics in mediating complex cognitive functions and behavior.
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Affiliation(s)
- Danilo Benozzo
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Giancarlo La Camera
- Department of Neurobiology and Behavior, Center for Neural Circuit Dynamics and Institute for Advanced Computational Science, State University of New York at Stony Brook, Stony Brook, NY, USA.
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.
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39
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40
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Saderi D, Schwartz ZP, Heller CR, Pennington JR, David SV. Dissociation of task engagement and arousal effects in auditory cortex and midbrain. eLife 2021; 10:e60153. [PMID: 33570493 PMCID: PMC7909948 DOI: 10.7554/elife.60153] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 02/10/2021] [Indexed: 12/18/2022] Open
Abstract
Both generalized arousal and engagement in a specific task influence sensory neural processing. To isolate effects of these state variables in the auditory system, we recorded single-unit activity from primary auditory cortex (A1) and inferior colliculus (IC) of ferrets during a tone detection task, while monitoring arousal via changes in pupil size. We used a generalized linear model to assess the influence of task engagement and pupil size on sound-evoked activity. In both areas, these two variables affected independent neural populations. Pupil size effects were more prominent in IC, while pupil and task engagement effects were equally likely in A1. Task engagement was correlated with larger pupil; thus, some apparent effects of task engagement should in fact be attributed to fluctuations in pupil size. These results indicate a hierarchy of auditory processing, where generalized arousal enhances activity in midbrain, and effects specific to task engagement become more prominent in cortex.
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Affiliation(s)
- Daniela Saderi
- Oregon Hearing Research Center, Oregon Health and Science UniversityPortlandUnited States
- Neuroscience Graduate Program, Oregon Health and Science UniversityPortlandUnited States
| | - Zachary P Schwartz
- Oregon Hearing Research Center, Oregon Health and Science UniversityPortlandUnited States
- Neuroscience Graduate Program, Oregon Health and Science UniversityPortlandUnited States
| | - Charles R Heller
- Oregon Hearing Research Center, Oregon Health and Science UniversityPortlandUnited States
- Neuroscience Graduate Program, Oregon Health and Science UniversityPortlandUnited States
| | - Jacob R Pennington
- Department of Mathematics and Statistics, Washington State UniversityVancouverUnited States
| | - Stephen V David
- Oregon Hearing Research Center, Oregon Health and Science UniversityPortlandUnited States
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41
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Procyk E, Fontanier V, Sarazin M, Delord B, Goussi C, Wilson CRE. The midcingulate cortex and temporal integration. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2021; 158:395-419. [PMID: 33785153 DOI: 10.1016/bs.irn.2020.12.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The ability to integrate information across time at multiple timescales is a vital element of adaptive behavior, because it provides the capacity to link events separated in time, extract useful information from previous events and actions, and to construct plans for behavior over time. Here we make the argument that this information integration capacity is a central function of the midcingulate cortex (MCC), by reviewing the anatomical, intrinsic network, neurophysiological, and behavioral properties of MCC. The MCC is the region of the medial wall situated dorsal to the corpus callosum and sometimes referred to as dACC. It is positioned within the densely connected core network of the primate brain, with a rich diversity of cognitive, somatomotor and autonomic connections. Furthermore, the MCC shows strong local network inhibition which appears to control the metastability of the region-an established feature of many cortical networks in which the neural dynamics move through a series of quasi-stationary states. We propose that the strong local inhibition in MCC leads to particularly long dynamic state durations, and so less frequent transitions. Apparently as a result of these anatomical features and synaptic and ionic determinants, the MCC cells display the longest neuronal timescales among a range of recorded cortical areas. We conclude that the anatomical position, intrinsic properties, and local network interactions of MCC make it a uniquely positioned cortical area to perform the integration of diverse information over time that is necessary for behavioral adaptation.
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Affiliation(s)
- Emmanuel Procyk
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France.
| | - Vincent Fontanier
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Matthieu Sarazin
- Institute of Intelligent Systems and Robotics (ISIR), Sorbonne Université, Centre National de la Recherche Scientifique, UMR 7222, Paris, France
| | - Bruno Delord
- Institute of Intelligent Systems and Robotics (ISIR), Sorbonne Université, Centre National de la Recherche Scientifique, UMR 7222, Paris, France
| | - Clément Goussi
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Charles R E Wilson
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France.
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42
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Muscarinic-Dependent miR-182 and QR2 Expression Regulation in the Anterior Insula Enables Novel Taste Learning. eNeuro 2020; 7:ENEURO.0067-20.2020. [PMID: 32217627 PMCID: PMC7266141 DOI: 10.1523/eneuro.0067-20.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 02/26/2020] [Indexed: 12/14/2022] Open
Abstract
In a similar manner to other learning paradigms, intact muscarinic acetylcholine receptor (mAChR) neurotransmission or protein synthesis regulation in the anterior insular cortex (aIC) is necessary for appetitive taste learning. Here we describe a parallel local molecular pathway, where GABAA receptor control of mAChR activation causes upregulation of miRNA-182 and quinone reductase 2 (QR2) mRNA destabilization in the rodent aIC. Damage to long-term memory by prevention of this process, with the use of mAChR antagonist scopolamine before novel taste learning, can be rescued by local QR2 inhibition, demonstrating that QR2 acts downstream of local muscarinic activation. Furthermore, we prove for the first time the presence of endogenous QR2 cofactors in the brain, establishing QR2 as a functional reductase there. In turn, we show that QR2 activity causes the generation of reactive oxygen species, leading to modulation in Kv2.1 redox state. QR2 expression reduction therefore is a previously unaccounted mode of mAChR-mediated inflammation reduction, and thus adds QR2 to the cadre of redox modulators in the brain. The concomitant reduction in QR2 activity during memory consolidation suggests a complementary mechanism to the well established molecular processes of this phase, by which the cortex gleans important information from general sensory stimuli. This places QR2 as a promising new target to tackle neurodegenerative inflammation and the associated impediment of novel memory formation in diseases such as Alzheimer’s disease.
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43
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Jordan ID, Park IM. Birhythmic Analog Circuit Maze: A Nonlinear Neurostimulation Testbed. ENTROPY 2020; 22:e22050537. [PMID: 33286310 PMCID: PMC7517031 DOI: 10.3390/e22050537] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/04/2020] [Accepted: 05/09/2020] [Indexed: 12/16/2022]
Abstract
Brain dynamics can exhibit narrow-band nonlinear oscillations and multistability. For a subset of disorders of consciousness and motor control, we hypothesized that some symptoms originate from the inability to spontaneously transition from one attractor to another. Using external perturbations, such as electrical pulses delivered by deep brain stimulation devices, it may be possible to induce such transition out of the pathological attractors. However, the induction of transition may be non-trivial, rendering the current open-loop stimulation strategies insufficient. In order to develop next-generation neural stimulators that can intelligently learn to induce attractor transitions, we require a platform to test the efficacy of such systems. To this end, we designed an analog circuit as a model for the multistable brain dynamics. The circuit spontaneously oscillates stably on two periods as an instantiation of a 3-dimensional continuous-time gated recurrent neural network. To discourage simple perturbation strategies, such as constant or random stimulation patterns from easily inducing transition between the stable limit cycles, we designed a state-dependent nonlinear circuit interface for external perturbation. We demonstrate the existence of nontrivial solutions to the transition problem in our circuit implementation.
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Affiliation(s)
- Ian D. Jordan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA;
- Institute for Advanced Computing Science, Stony Brook University, Stony Brook, NY 11794, USA
| | - Il Memming Park
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA;
- Institute for Advanced Computing Science, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA
- Correspondence:
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