1
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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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2
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Insanally MN, Albanna BF, Toth J, DePasquale B, Fadaei SS, Gupta T, Lombardi O, Kuchibhotla K, Rajan K, Froemke RC. Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance. Nat Commun 2024; 15:6023. [PMID: 39019848 PMCID: PMC11255273 DOI: 10.1038/s41467-024-49895-6] [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: 05/19/2023] [Accepted: 06/20/2024] [Indexed: 07/19/2024] Open
Abstract
Neuronal responses during behavior are diverse, ranging from highly reliable 'classical' responses to irregular 'non-classically responsive' firing. While a continuum of response properties is observed across neural systems, little is known about the synaptic origins and contributions of diverse responses to network function, perception, and behavior. To capture the heterogeneous responses measured from auditory cortex of rodents performing a frequency recognition task, we use a novel task-performing spiking recurrent neural network incorporating spike-timing-dependent plasticity. Reliable and irregular units contribute differentially to task performance via output and recurrent connections, respectively. Excitatory plasticity shifts the response distribution while inhibition constrains its diversity. Together both improve task performance with full network engagement. The same local patterns of synaptic inputs predict spiking response properties of network units and auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus, diverse neural responses contribute to network function and emerge from synaptic plasticity rules.
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Affiliation(s)
- Michele N Insanally
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
| | - Badr F Albanna
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
| | - Jade Toth
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Brian DePasquale
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Saba Shokat Fadaei
- Skirball Institute for Biomolecular Medicine, New York University Grossman School of Medicine, New York, NY, 10016, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY, 10016, USA
- Department of Neuroscience, New York University Grossman School of Medicine, New York, NY, 10016, USA
- Department of Physiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Trisha Gupta
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Olivia Lombardi
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Kishore Kuchibhotla
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kanaka Rajan
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
- Kempner Institute, Harvard University, Cambridge, MA, 02138, USA
| | - Robert C Froemke
- Skirball Institute for Biomolecular Medicine, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Neuroscience, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Physiology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Center for Neural Science, New York University, New York, NY, 10003, USA.
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3
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Waitzmann F, Wu YK, Gjorgjieva J. Top-down modulation in canonical cortical circuits with short-term plasticity. Proc Natl Acad Sci U S A 2024; 121:e2311040121. [PMID: 38593083 PMCID: PMC11032497 DOI: 10.1073/pnas.2311040121] [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: 06/30/2023] [Accepted: 02/14/2024] [Indexed: 04/11/2024] Open
Abstract
Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.
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Affiliation(s)
- Felix Waitzmann
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
| | - Yue Kris Wu
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
| | - Julijana Gjorgjieva
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
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4
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Politi A, Torcini A. A robust balancing mechanism for spiking neural networks. CHAOS (WOODBURY, N.Y.) 2024; 34:041102. [PMID: 38639569 DOI: 10.1063/5.0199298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/03/2024] [Indexed: 04/20/2024]
Abstract
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons.
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Affiliation(s)
- Antonio Politi
- Institute for Complex Systems and Mathematical Biology and Department of Physics, Aberdeen AB24 3UE, United Kingdom
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
| | - Alessandro Torcini
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
- Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS UMR 8089, 95302 Cergy-Pontoise cedex, France
- INFN Sezione di Firenze, Via Sansone 1 50019 Sesto Fiorentino, Italy
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5
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Fitz H, Hagoort P, Petersson KM. Neurobiological Causal Models of Language Processing. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:225-247. [PMID: 38645618 PMCID: PMC11025648 DOI: 10.1162/nol_a_00133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/18/2023] [Indexed: 04/23/2024]
Abstract
The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.
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Affiliation(s)
- Hartmut Fitz
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Peter Hagoort
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Karl Magnus Petersson
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Faculty of Medicine and Biomedical Sciences, University of Algarve, Faro, Portugal
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6
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Noda T, Takahashi H. Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat. Cereb Cortex 2024; 34:bhad428. [PMID: 37955660 PMCID: PMC10793590 DOI: 10.1093/cercor/bhad428] [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: 09/04/2022] [Revised: 10/10/2023] [Accepted: 10/25/2023] [Indexed: 11/14/2023] Open
Abstract
The awake cortex is characterized by a higher level of ongoing spontaneous activity, but it has a better detectability of weak sensory inputs than the anesthetized cortex. However, the computational mechanism underlying this paradoxical nature of awake neuronal activity remains to be elucidated. Here, we propose a hypothetical stochastic resonance, which improves the signal-to-noise ratio (SNR) of weak sensory inputs through nonlinear relations between ongoing spontaneous activities and sensory-evoked activities. Prestimulus and tone-evoked activities were investigated via in vivo extracellular recording with a dense microelectrode array covering the entire auditory cortex in rats in both awake and anesthetized states. We found that tone-evoked activities increased supralinearly with the prestimulus activity level in the awake state and that the SNR of weak stimulus representation was optimized at an intermediate level of prestimulus ongoing activity. Furthermore, the temporally intermittent firing pattern, but not the trial-by-trial reliability or the fluctuation of local field potential, was identified as a relevant factor for SNR improvement. Since ongoing activity differs among neurons, hypothetical stochastic resonance or "sparse network stochastic resonance" might offer beneficial SNR improvement at the single-neuron level, which is compatible with the sparse representation in the sensory cortex.
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Affiliation(s)
- Takahiro Noda
- Department of Mechano-informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Hirokazu Takahashi
- Department of Mechano-informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
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7
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Burns TF. Does inhibitory (dys)function account for involuntary autobiographical memory and déjà vu experience? Behav Brain Sci 2023; 46:e360. [PMID: 37961769 DOI: 10.1017/s0140525x23000146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
External cues and internal configuration states are the likely instigators of involuntary autobiographical memories (IAMs) and déjà vu experience. Indeed, Barzykowski and Moulin discuss relevant neuroscientific evidence in this direction. A complementary line of enquiry and evidence is the study of inhibition and its role in memory retrieval, and particularly how its (dys)function may contribute to IAMs and déjà vu.
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Affiliation(s)
- Thomas F Burns
- Okinawa Institute of Science and Technology Graduate University, Onna-son, Kunigami-gun, Okinawa, Japan ://tfburns.com/
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8
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Forsyth JK, Bearden CE. Rethinking the First Episode of Schizophrenia: Identifying Convergent Mechanisms During Development and Moving Toward Prediction. Am J Psychiatry 2023; 180:792-804. [PMID: 37908094 DOI: 10.1176/appi.ajp.20230736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Affiliation(s)
- Jennifer K Forsyth
- Department of Psychology, University of Washington, Seattle (Forsyth); Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Behavioral Sciences, and Department of Psychology, University of California, Los Angeles (Bearden)
| | - Carrie E Bearden
- Department of Psychology, University of Washington, Seattle (Forsyth); Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Behavioral Sciences, and Department of Psychology, University of California, Los Angeles (Bearden)
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9
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Levy ERJ, Carrillo-Segura S, Park EH, Redman WT, Hurtado JR, Chung S, Fenton AA. A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields. Cell Rep 2023; 42:113142. [PMID: 37742193 PMCID: PMC10842170 DOI: 10.1016/j.celrep.2023.113142] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 06/14/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023] Open
Abstract
Hippocampus place cell discharge is temporally unreliable across seconds and days, and place fields are multimodal, suggesting an "ensemble cofiring" spatial coding hypothesis with manifold dynamics that does not require reliable spatial tuning, in contrast to hypotheses based on place field (spatial tuning) stability. We imaged mouse CA1 (cornu ammonis 1) ensembles in two environments across three weeks to evaluate these coding hypotheses. While place fields "remap," being more distinct between than within environments, coactivity relationships generally change less. Decoding location and environment from 1-s ensemble location-specific activity is effective and improves with experience. Decoding environment from cell-pair coactivity relationships is also effective and improves with experience, even after removing place tuning. Discriminating environments from 1-s ensemble coactivity relies crucially on the cells with the most anti-coactive cell-pair relationships because activity is internally organized on a low-dimensional manifold of non-linear coactivity relationships that intermittently reregisters to environments according to the anti-cofiring subpopulation activity.
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Affiliation(s)
| | - Simón Carrillo-Segura
- Center for Neural Science, New York University, New York, NY 10003, USA; Graduate Program in Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
| | - Eun Hye Park
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - William Thomas Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, NY 10003, USA; Flatiron Institute Center for Computational Neuroscience, New York, NY 10010, USA
| | - André Antonio Fenton
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute at the NYU Langone Medical Center, New York, NY 10016, USA.
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10
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Roethler O, Zohar E, Cohen-Kashi Malina K, Bitan L, Gabel HW, Spiegel I. Single genomic enhancers drive experience-dependent GABAergic plasticity to maintain sensory processing in the adult cortex. Neuron 2023; 111:2693-2708.e8. [PMID: 37354902 DOI: 10.1016/j.neuron.2023.05.026] [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: 10/03/2022] [Revised: 03/29/2023] [Accepted: 05/30/2023] [Indexed: 06/26/2023]
Abstract
Experience-dependent plasticity of synapses modulates information processing in neural circuits and is essential for cognitive functions. The genome, via non-coding enhancers, was proposed to control information processing and circuit plasticity by regulating experience-induced transcription of genes that modulate specific sets of synapses. To test this idea, we analyze here the cellular and circuit functions of the genomic mechanisms that control the experience-induced transcription of Igf1 (insulin-like growth factor 1) in vasoactive intestinal peptide (VIP) interneurons (INs) in the visual cortex of adult mice. We find that two sensory-induced enhancers selectively and cooperatively drive the activity-induced transcription of Igf1 to thereby promote GABAergic inputs onto VIP INs and to homeostatically control the ratio between excitation and inhibition (E/I ratio)-in turn, this restricts neural activity in VIP INs and principal excitatory neurons and maintains spatial frequency tuning. Thus, enhancer-mediated activity-induced transcription maintains sensory processing in the adult cortex via homeostatic modulation of E/I ratio.
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Affiliation(s)
- Ori Roethler
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Zohar
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Katayun Cohen-Kashi Malina
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Lidor Bitan
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Harrison Wren Gabel
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Ivo Spiegel
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel.
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11
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Kim CM, Finkelstein A, Chow CC, Svoboda K, Darshan R. Distributing task-related neural activity across a cortical network through task-independent connections. Nat Commun 2023; 14:2851. [PMID: 37202424 DOI: 10.1038/s41467-023-38529-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 05/05/2023] [Indexed: 05/20/2023] Open
Abstract
Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.
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Affiliation(s)
- Christopher M Kim
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Arseny Finkelstein
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Carson C Chow
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Ran Darshan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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12
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Bergoin R, Torcini A, Deco G, Quoy M, Zamora-López G. Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli. Sci Rep 2023; 13:6949. [PMID: 37117236 PMCID: PMC10147639 DOI: 10.1038/s41598-023-34165-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/25/2023] [Indexed: 04/30/2023] Open
Abstract
Brain circuits display modular architecture at different scales of organization. Such neural assemblies are typically associated to functional specialization but the mechanisms leading to their emergence and consolidation still remain elusive. In this paper we investigate the role of inhibition in structuring new neural assemblies driven by the entrainment to various inputs. In particular, we focus on the role of partially synchronized dynamics for the creation and maintenance of structural modules in neural circuits by considering a network of excitatory and inhibitory [Formula: see text]-neurons with plastic Hebbian synapses. The learning process consists of an entrainment to temporally alternating stimuli that are applied to separate regions of the network. This entrainment leads to the emergence of modular structures. Contrary to common practice in artificial neural networks-where the acquired weights are typically frozen after the learning session-we allow for synaptic adaptation even after the learning phase. We find that the presence of inhibitory neurons in the network is crucial for the emergence and the post-learning consolidation of the modular structures. Indeed networks made of purely excitatory neurons or of neurons not respecting Dale's principle are unable to form or to maintain the modular architecture induced by the stimuli. We also demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be consolidated, supporting the idea that inhibition has a direct impact on the memory capacity of the neural network.
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Affiliation(s)
- Raphaël Bergoin
- ETIS, UMR 8051, ENSEA, CY Cergy Paris Université, CNRS, 6 Av. du Ponceau, 95000, Cergy-Pontoise, France.
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain.
| | - Alessandro Torcini
- Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, 2 Av. Adolphe Chauvin, 95032, Cergy-Pontoise, France
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain
- Instituciò Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Spain
| | - Mathias Quoy
- ETIS, UMR 8051, ENSEA, CY Cergy Paris Université, CNRS, 6 Av. du Ponceau, 95000, Cergy-Pontoise, France
- IPAL, CNRS, 1 Fusionopolis Way #21-01 Connexis (South Tower), Singapore, 138632, Singapore
| | - Gorka Zamora-López
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain
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13
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McFarlan AR, Chou CYC, Watanabe A, Cherepacha N, Haddad M, Owens H, Sjöström PJ. The plasticitome of cortical interneurons. Nat Rev Neurosci 2023; 24:80-97. [PMID: 36585520 DOI: 10.1038/s41583-022-00663-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/31/2022]
Abstract
Hebb postulated that, to store information in the brain, assemblies of excitatory neurons coding for a percept are bound together via associative long-term synaptic plasticity. In this view, it is unclear what role, if any, is carried out by inhibitory interneurons. Indeed, some have argued that inhibitory interneurons are not plastic. Yet numerous recent studies have demonstrated that, similar to excitatory neurons, inhibitory interneurons also undergo long-term plasticity. Here, we discuss the many diverse forms of long-term plasticity that are found at inputs to and outputs from several types of cortical inhibitory interneuron, including their plasticity of intrinsic excitability and their homeostatic plasticity. We explain key plasticity terminology, highlight key interneuron plasticity mechanisms, extract overarching principles and point out implications for healthy brain functionality as well as for neuropathology. We introduce the concept of the plasticitome - the synaptic plasticity counterpart to the genome or the connectome - as well as nomenclature and definitions for dealing with this rich diversity of plasticity. We argue that the great diversity of interneuron plasticity rules is best understood at the circuit level, for example as a way of elucidating how the credit-assignment problem is solved in deep biological neural networks.
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Affiliation(s)
- Amanda R McFarlan
- Centre for Research in Neuroscience, Department of Medicine, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.,Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
| | - Christina Y C Chou
- Centre for Research in Neuroscience, Department of Medicine, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.,Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
| | - Airi Watanabe
- Centre for Research in Neuroscience, Department of Medicine, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.,Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
| | - Nicole Cherepacha
- Centre for Research in Neuroscience, Department of Medicine, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Maria Haddad
- Centre for Research in Neuroscience, Department of Medicine, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.,Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
| | - Hannah Owens
- Centre for Research in Neuroscience, Department of Medicine, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.,Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
| | - P Jesper Sjöström
- Centre for Research in Neuroscience, Department of Medicine, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.
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14
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Goldt S, Krzakala F, Zdeborová L, Brunel N. Bayesian reconstruction of memories stored in neural networks from their connectivity. PLoS Comput Biol 2023; 19:e1010813. [PMID: 36716332 PMCID: PMC9910750 DOI: 10.1371/journal.pcbi.1010813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 02/09/2023] [Accepted: 12/12/2022] [Indexed: 02/01/2023] Open
Abstract
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in a recurrent network of neurons, given its synaptic connectivity matrix. Here, we address this question by determining when solving such an inference problem is theoretically possible in specific attractor network models and by providing a practical algorithm to do so. The algorithm builds on ideas from statistical physics to perform approximate Bayesian inference and is amenable to exact analysis. We study its performance on three different models, compare the algorithm to standard algorithms such as PCA, and explore the limitations of reconstructing stored patterns from synaptic connectivity.
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Affiliation(s)
- Sebastian Goldt
- International School of Advanced Studies (SISSA), Trieste, Italy
- * E-mail:
| | - Florent Krzakala
- IdePHICS laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Lenka Zdeborová
- SPOC laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Nicolas Brunel
- Department of Neurobiology, Duke University, Durham, North Carolina, United States of America
- Department of Physics, Duke University, Durham, North Carolina, United States of America
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15
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Shao F, Shen Z. How can artificial neural networks approximate the brain? Front Psychol 2023; 13:970214. [PMID: 36698593 PMCID: PMC9868316 DOI: 10.3389/fpsyg.2022.970214] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 11/28/2022] [Indexed: 01/11/2023] Open
Abstract
The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions to be investigated further for the interdisciplinary field of brain simulation. Even though brain is a super-complex system with 1011 neurons, its intelligence does depend rather on the neuronal type and their energy supply mode than the number of neurons. It might be possible for ANN development to follow a new direction that is a combination of multiple modules with different architecture principle and multiple computation, rather than very large scale of neural networks with much more uniformed units and hidden layers.
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Affiliation(s)
- Feng Shao
- Beijing Key Laboratory of Behavior and Mental Health, School of Psychological and Cognitive Sciences, Peking University, Beijing, China
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16
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KASAI H. Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition. PROCEEDINGS OF THE JAPAN ACADEMY. SERIES B, PHYSICAL AND BIOLOGICAL SCIENCES 2023; 99:254-305. [PMID: 37821392 PMCID: PMC10749395 DOI: 10.2183/pjab.99.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 07/11/2023] [Indexed: 10/13/2023]
Abstract
Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.
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Affiliation(s)
- Haruo KASAI
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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17
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Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles. Biosystems 2023; 223:104813. [PMID: 36460172 DOI: 10.1016/j.biosystems.2022.104813] [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: 05/19/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 12/02/2022]
Abstract
Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The "eigenangle test" quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov-Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.
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18
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Huiskamp M, Yaqub M, van Lingen MR, Pouwels PJW, de Ruiter LRJ, Killestein J, Schwarte LA, Golla SSV, van Berckel BNM, Boellaard R, Geurts JJG, Hulst HE. Cognitive performance in multiple sclerosis: what is the role of the gamma-aminobutyric acid system? Brain Commun 2023; 5:fcad140. [PMID: 37180993 PMCID: PMC10174207 DOI: 10.1093/braincomms/fcad140] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/26/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023] Open
Abstract
Cognitive impairment occurs in 40-65% of persons with multiple sclerosis and may be related to alterations in glutamatergic and GABAergic neurotransmission. Therefore, the aim of this study was to determine how glutamatergic and GABAergic changes relate to cognitive functioning in multiple sclerosis in vivo. Sixty persons with multiple sclerosis (mean age 45.5 ± 9.6 years, 48 females, 51 relapsing-remitting multiple sclerosis) and 22 age-matched healthy controls (45.6 ± 22.0 years, 17 females) underwent neuropsychological testing and MRI. Persons with multiple sclerosis were classified as cognitively impaired when scoring at least 1.5 standard deviations below normative scores on ≥30% of tests. Glutamate and GABA concentrations were determined in the right hippocampus and bilateral thalamus using magnetic resonance spectroscopy. GABA-receptor density was assessed using quantitative [11C]flumazenil positron emission tomography in a subset of participants. Positron emission tomography outcome measures were the influx rate constant (a measure predominantly reflecting perfusion) and volume of distribution, which is a measure of GABA-receptor density. Twenty persons with multiple sclerosis (33%) fulfilled the criteria for cognitive impairment. No differences were observed in glutamate or GABA concentrations between persons with multiple sclerosis and healthy controls, or between cognitively preserved, impaired and healthy control groups. Twenty-two persons with multiple sclerosis (12 cognitively preserved and 10 impaired) and 10 healthy controls successfully underwent [11C]flumazenil positron emission tomography. Persons with multiple sclerosis showed a lower influx rate constant in the thalamus, indicating lower perfusion. For the volume of distribution, persons with multiple sclerosis showed higher values than controls in deep grey matter, reflecting increased GABA-receptor density. When comparing cognitively impaired and preserved patients to controls, the preserved group showed a significantly higher volume of distribution in cortical and deep grey matter and hippocampus. Positive correlations were observed between both positron emission tomography measures and information processing speed in the multiple sclerosis group only. Whereas concentrations of glutamate and GABA did not differ between multiple sclerosis and control nor between cognitively impaired, preserved and control groups, increased GABA-receptor density was observed in preserved persons with multiple sclerosis that was not seen in cognitively impaired patients. In addition, GABA-receptor density correlated to cognition, in particular with information processing speed. This could indicate that GABA-receptor density is upregulated in the cognitively preserved phase of multiple sclerosis as a means to regulate neurotransmission and potentially preserve cognitive functioning.
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Affiliation(s)
- Marijn Huiskamp
- Correspondence to: M. Huiskamp Department of Anatomy & Neurosciences Amsterdam UMC, Location Vrije Universiteit PO Box 7057, 1007 MB Amsterdam, The Netherlands E-mail:
| | - Maqsood Yaqub
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Marike R van Lingen
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Petra J W Pouwels
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Lodewijk R J de Ruiter
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Joep Killestein
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Lothar A Schwarte
- Department of Anesthesiology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Ronald Boellaard
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Jeroen J G Geurts
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Hanneke E Hulst
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, 2333 AK, The Netherlands
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19
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Shu S, Xu SY, Ye L, Liu Y, Cao X, Jia JQ, Bian HJ, Liu Y, Zhu XL, Xu Y. Prefrontal parvalbumin interneurons deficits mediate early emotional dysfunction in Alzheimer's disease. Neuropsychopharmacology 2023; 48:391-401. [PMID: 36229597 PMCID: PMC9750960 DOI: 10.1038/s41386-022-01435-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/19/2022] [Accepted: 08/16/2022] [Indexed: 12/26/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease and has an insidious onset. Exploring the characteristics and mechanism of the early symptoms of AD plays a critical role in the early diagnosis and intervention of AD. Here we found that depressive-like behavior and short-term spatial memory dysfunction appeared in APPswe/PS1dE9 mice (AD mice) as early as 9-11 weeks of age. Electrophysiological analysis revealed excitatory/inhibitory (E/I) imbalance in the prefrontal cortex (PFC). This E/I imbalance was induced by significant reduction in the number and activity of parvalbumin interneurons (PV+ INs) in this region. Furthermore, optogenetic and chemogenetic activation of residual PV+ INs effectively ameliorated depressive-like behavior and rescued short-term spatial memory in AD mice. These results suggest the PFC is selectively vulnerable in the early stage of AD and prefrontal PV+ INs deficits play a key role in the occurrence and development of early symptoms of AD.
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Affiliation(s)
- Shu Shu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Institute of Brain Sciences, Nanjing University, Nanjing, 210093, Jiangsu, PR China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, 210008, Jiangsu, PR China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, 210008, Jiangsu, PR China
| | - Si-Yi Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Zhenjiang, 212013, Jiangsu, PR China
| | - Lei Ye
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
| | - Yi Liu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Institute of Brain Sciences, Nanjing University, Nanjing, 210093, Jiangsu, PR China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, 210008, Jiangsu, PR China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, 210008, Jiangsu, PR China
| | - Xiang Cao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Institute of Brain Sciences, Nanjing University, Nanjing, 210093, Jiangsu, PR China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, 210008, Jiangsu, PR China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, 210008, Jiangsu, PR China
| | - Jun-Qiu Jia
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
| | - Hui-Jie Bian
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, PR China
| | - Ying Liu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
| | - Xiao-Lei Zhu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Institute of Brain Sciences, Nanjing University, Nanjing, 210093, Jiangsu, PR China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, PR China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, 210008, Jiangsu, PR China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, 210008, Jiangsu, PR China
| | - Yun Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, 210008, Jiangsu, PR China.
- Institute of Brain Sciences, Nanjing University, Nanjing, 210093, Jiangsu, PR China.
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, PR China.
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, 210008, Jiangsu, PR China.
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, 210008, Jiangsu, PR China.
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20
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Wu YK, Miehl C, Gjorgjieva J. Regulation of circuit organization and function through inhibitory synaptic plasticity. Trends Neurosci 2022; 45:884-898. [PMID: 36404455 DOI: 10.1016/j.tins.2022.10.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 11/15/2022]
Abstract
Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.
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Affiliation(s)
- Yue Kris Wu
- School of Life Sciences, Technical University of Munich, Freising, Germany; Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Christoph Miehl
- School of Life Sciences, Technical University of Munich, Freising, Germany; Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Julijana Gjorgjieva
- School of Life Sciences, Technical University of Munich, Freising, Germany; Max Planck Institute for Brain Research, Frankfurt, Germany.
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21
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Bosten JM, Coen-Cagli R, Franklin A, Solomon SG, Webster MA. Calibrating Vision: Concepts and Questions. Vision Res 2022; 201:108131. [PMID: 37139435 PMCID: PMC10151026 DOI: 10.1016/j.visres.2022.108131] [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] [Indexed: 11/08/2022]
Abstract
The idea that visual coding and perception are shaped by experience and adjust to changes in the environment or the observer is universally recognized as a cornerstone of visual processing, yet the functions and processes mediating these calibrations remain in many ways poorly understood. In this article we review a number of facets and issues surrounding the general notion of calibration, with a focus on plasticity within the encoding and representational stages of visual processing. These include how many types of calibrations there are - and how we decide; how plasticity for encoding is intertwined with other principles of sensory coding; how it is instantiated at the level of the dynamic networks mediating vision; how it varies with development or between individuals; and the factors that may limit the form or degree of the adjustments. Our goal is to give a small glimpse of an enormous and fundamental dimension of vision, and to point to some of the unresolved questions in our understanding of how and why ongoing calibrations are a pervasive and essential element of vision.
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Affiliation(s)
| | - Ruben Coen-Cagli
- Department of Systems Computational Biology, and Dominick P. Purpura Department of Neuroscience, and Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx NY
| | | | - Samuel G Solomon
- Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, UK
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22
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Chambers AR, Aschauer DF, Eppler JB, Kaschube M, Rumpel S. A stable sensory map emerges from a dynamic equilibrium of neurons with unstable tuning properties. Cereb Cortex 2022; 33:5597-5612. [PMID: 36418925 PMCID: PMC10152095 DOI: 10.1093/cercor/bhac445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/25/2022] Open
Abstract
Abstract
Recent long-term measurements of neuronal activity have revealed that, despite stability in large-scale topographic maps, the tuning properties of individual cortical neurons can undergo substantial reformatting over days. To shed light on this apparent contradiction, we captured the sound response dynamics of auditory cortical neurons using repeated 2-photon calcium imaging in awake mice. We measured sound-evoked responses to a set of pure tone and complex sound stimuli in more than 20,000 auditory cortex neurons over several days. We found that a substantial fraction of neurons dropped in and out of the population response. We modeled these dynamics as a simple discrete-time Markov chain, capturing the continuous changes in responsiveness observed during stable behavioral and environmental conditions. Although only a minority of neurons were driven by the sound stimuli at a given time point, the model predicts that most cells would at least transiently become responsive within 100 days. We observe that, despite single-neuron volatility, the population-level representation of sound frequency was stably maintained, demonstrating the dynamic equilibrium underlying the tonotopic map. Our results show that sensory maps are maintained by shifting subpopulations of neurons “sharing” the job of creating a sensory representation.
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Affiliation(s)
- Anna R Chambers
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University Mainz , Duesbergweg 6, Mainz 55128 , Germany
| | - Dominik F Aschauer
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University Mainz , Duesbergweg 6, Mainz 55128 , Germany
| | - Jens-Bastian Eppler
- Frankfurt Institute for Advanced Studies and Department of Computer Science, Goethe University Frankfurt , Ruth-Moufang-Straße 1, Frankfurt am Main 60438 , Germany
| | - Matthias Kaschube
- Frankfurt Institute for Advanced Studies and Department of Computer Science, Goethe University Frankfurt , Ruth-Moufang-Straße 1, Frankfurt am Main 60438 , Germany
| | - Simon Rumpel
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University Mainz , Duesbergweg 6, Mainz 55128 , Germany
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23
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Huiskamp M, Kiljan S, Kulik S, Witte ME, Jonkman LE, Gjm Bol J, Schenk GJ, Hulst HE, Tewarie P, Schoonheim MM, Geurts JJ. Inhibitory synaptic loss drives network changes in multiple sclerosis: An ex vivo to in silico translational study. Mult Scler 2022; 28:2010-2019. [PMID: 36189828 PMCID: PMC9574900 DOI: 10.1177/13524585221125381] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background: Synaptic and neuronal loss contribute to network dysfunction and disability
in multiple sclerosis (MS). However, it is unknown whether excitatory or
inhibitory synapses and neurons are more vulnerable and how their losses
impact network functioning. Objective: To quantify excitatory and inhibitory synapses and neurons and to investigate
how synaptic loss affects network functioning through computational
modeling. Methods: Using immunofluorescent staining and confocal microscopy, densities of
glutamatergic and GABAergic synapses and neurons were compared between
post-mortem MS and non-neurological control cases. Then, a corticothalamic
biophysical model was employed to study how MS-induced excitatory and
inhibitory synaptic loss affect network functioning. Results: In layer VI of normal-appearing MS cortex, excitatory and inhibitory synaptic
densities were significantly lower than controls (reductions up to 14.9%),
but demyelinated cortex showed larger losses of inhibitory synapses (29%).
In our computational model, reducing inhibitory synapses impacted the
network most, leading to a disinhibitory increase in neuronal activity and
connectivity. Conclusion: In MS, excitatory and inhibitory synaptic losses were observed, predominantly
for inhibitory synapses in demyelinated cortex. Inhibitory synaptic loss
affected network functioning most, leading to increased neuronal activity
and connectivity. As network disinhibition relates to cognitive impairment,
inhibitory synaptic loss seems particularly relevant in MS.
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Affiliation(s)
- Marijn Huiskamp
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Svenja Kiljan
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Shanna Kulik
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Maarteen E Witte
- Molecular Cell Biology and Inflammation, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Laura E Jonkman
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - John Gjm Bol
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Geert J Schenk
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Hanneke E Hulst
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands/Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Prejaas Tewarie
- Neurology, MS center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands/Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Jeroen Jg Geurts
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
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24
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Köksal Ersöz E, Chossat P, Krupa M, Lavigne F. Dynamic branching in a neural network model for probabilistic prediction of sequences. J Comput Neurosci 2022; 50:537-557. [PMID: 35948839 DOI: 10.1007/s10827-022-00830-y] [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: 01/16/2022] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.
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Affiliation(s)
- Elif Köksal Ersöz
- Univ Rennes, INSERM, LTSI - UMR 1099, Campus Beaulieu, Rennes, F-35000, France. .,Project Team MathNeuro, INRIA-CNRS-UNS, 2004 route des Lucioles-BP 93, Sophia Antipolis, 06902, France.
| | - Pascal Chossat
- Project Team MathNeuro, INRIA-CNRS-UNS, 2004 route des Lucioles-BP 93, Sophia Antipolis, 06902, France.,Université Côte d'Azur, Laboratoire Jean-Alexandre Dieudonné, Campus Valrose, Nice, 06300, France
| | - Martin Krupa
- Project Team MathNeuro, INRIA-CNRS-UNS, 2004 route des Lucioles-BP 93, Sophia Antipolis, 06902, France.,Université Côte d'Azur, Laboratoire Jean-Alexandre Dieudonné, Campus Valrose, Nice, 06300, France
| | - Frédéric Lavigne
- Université Côte d'Azur, CNRS-BCL, Campus Saint Jean d'Angely, Nice, 06300, France
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25
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Hennig MH. The sloppy relationship between neural circuit structure and function. J Physiol 2022. [PMID: 35876720 DOI: 10.1113/jp282757] [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: 04/14/2022] [Accepted: 07/20/2022] [Indexed: 11/08/2022] Open
Abstract
Investigating and describing the relationships between the structure of a circuit and its function has a long tradition in neuroscience. Since neural circuits acquire their structure through sophisticated developmental programmes, and memories and experiences are maintained through synaptic modification, it is to be expected that structure is closely linked to function. Recent findings challenge this hypothesis from three different angles: Function does not strongly constrain circuit parameters, many parameters in neural circuits are irrelevant and contribute little to function, and circuit parameters are unstable and subject to constant random drift. At the same time however, recent work also showed that dynamics in neural circuit activity that is related to function are robust over time and across individuals. Here this apparent contradiction is addressed by considering the properties of neural manifolds that restrict circuit activity to functionally relevant subspaces, and it will be suggested that degenerate, anisotropic and unstable parameter spaces are a closely related to the structure and implementation of functionally relevant neural manifolds. Abstract figure legend What are the relationships between noisy and highly variable microscopic neural circuit variables on the one hand and the generation of behaviour on the other? Here it is proposed that an intermediate level of description exists where this relationship can be understood in terms of low-dimensional dynamics. Recordings of neural activity during unconstrained behaviour and the development of new machine learning methods will help to uncover these links. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Matthias H Hennig
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh
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26
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Redman WT, Wolcott NS, Montelisciani L, Luna G, Marks TD, Sit KK, Yu CH, Smith S, Goard MJ. Long-term transverse imaging of the hippocampus with glass microperiscopes. eLife 2022; 11:75391. [PMID: 35775393 PMCID: PMC9249394 DOI: 10.7554/elife.75391] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 06/12/2022] [Indexed: 11/19/2022] Open
Abstract
The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.
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Affiliation(s)
- William T Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, United States
| | - Nora S Wolcott
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, United States
| | - Luca Montelisciani
- Cognitive and Systems Neuroscience Group, University of Amsterdam, Amsterdam, Netherlands
| | - Gabriel Luna
- Neuroscience Research Institute, University of California, Santa Barbara, United States
| | - Tyler D Marks
- Neuroscience Research Institute, University of California, Santa Barbara, United States
| | - Kevin K Sit
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, United States
| | - Che-Hang Yu
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, United States
| | - Spencer Smith
- Neuroscience Research Institute, University of California, Santa Barbara, United States.,Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, United States
| | - Michael J Goard
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, United States.,Neuroscience Research Institute, University of California, Santa Barbara, United States.,Department of Psychological and Brain Sciences, University of California, Santa Barbara, United States
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27
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Homeostatic plasticity and excitation-inhibition balance: The good, the bad, and the ugly. Curr Opin Neurobiol 2022; 75:102553. [PMID: 35594578 DOI: 10.1016/j.conb.2022.102553] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/15/2022] [Accepted: 04/13/2022] [Indexed: 12/12/2022]
Abstract
In this review, we discuss the significance of the synaptic excitation/inhibition (E/I) balance in the context of homeostatic plasticity, whose primary goal is thought to maintain neuronal firing rates at a set point. We first provide an overview of the processes through which patterned input activity drives synaptic E/I tuning and maturation of circuits during development. Next, we emphasize the importance of the E/I balance at the synaptic level (homeostatic control of message reception) as a means to achieve the goal (homeostatic control of information transmission) at the network level and consider how compromised homeostatic plasticity associated with neurological diseases leads to hyperactivity, network instability, and ultimately improper information processing. Lastly, we highlight several pathological conditions related to sensory deafferentation and describe how, in some cases, homeostatic compensation without appropriate sensory inputs can result in phantom perceptions.
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28
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Nashef A, Cohen O, Perlmutter SI, Prut Y. A cerebellar origin of feedforward inhibition to the motor cortex in non-human primates. Cell Rep 2022; 39:110803. [PMID: 35545040 DOI: 10.1016/j.celrep.2022.110803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/02/2022] [Accepted: 04/20/2022] [Indexed: 02/07/2023] Open
Abstract
Voluntary movements are driven by coordinated activity across a large population of motor cortical neurons. Formation of this activity is controlled by local interactions and long-range inputs. How remote areas of the brain communicate with motor cortical neurons to effectively drive movement remains unclear. We address this question by studying the cerebellar-thalamocortical system. We find that thalamic input to the motor cortex triggers feedforward inhibition by contacting inhibitory cells via highly effective GluR2-lacking AMPA receptors and that, during task performance, the activity of parvalbumin (PV) and pyramidal cells exhibits relations comparable with movement parameters. We also find that the movement-related activity of PV interneurons precedes firing of pyramidal cells. This counterintuitive sequence of events, where inhibitory cells are recruited more strongly and before excitatory cells, may amplify the cortical effect of cerebellar signals in a way that exceeds their sheer synaptic efficacy by suppressing other inputs.
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Affiliation(s)
- Abdulraheem Nashef
- Department of Medical Neurobiology, IMRIC and ELSC, The Hebrew University, Hadassah Medical School, Jerusalem 9112102, Israel
| | - Oren Cohen
- Department of Medical Neurobiology, IMRIC and ELSC, The Hebrew University, Hadassah Medical School, Jerusalem 9112102, Israel
| | - Steve I Perlmutter
- Department of Physiology & Biophysics and Washington National Primate Research Center, University of Washington, Box 357330, Seattle, WA 98195, USA
| | - Yifat Prut
- Department of Medical Neurobiology, IMRIC and ELSC, The Hebrew University, Hadassah Medical School, Jerusalem 9112102, Israel.
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29
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Fang X, Duan S, Wang L. Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory. Front Neurosci 2022; 16:885322. [PMID: 35592261 PMCID: PMC9110805 DOI: 10.3389/fnins.2022.885322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/13/2022] [Indexed: 11/30/2022] Open
Abstract
The Izhikevich (IZH) spiking neuron model can display spiking and bursting behaviors of neurons. Based on the switching property and bio-plausibility of the memristor, the memristive Izhikevich (MIZH) spiking neuron model is built. Firstly, the MIZH spiking model is introduced and used to generate 23 spiking patterns. We compare the 23 spiking patterns produced by the IZH and MIZH spiking models. Secondly, the MIZH spiking model actively reproduces various neuronal behaviors, including the excitatory cortical neurons, the inhibitory cortical neurons, and other cortical neurons. Finally, the collective dynamic activities of the MIZH neuronal network are performed, and the MIZH oscillatory network is constructed. Experimental results illustrate that the constructed MIZH spiking neuron model performs high firing frequency and good frequency adaptation. The model can easily simulate various spiking and bursting patterns of distinct neurons in the brain. The MIZH neuronal network realizes the synchronous and asynchronous collective behaviors. The MIZH oscillatory network can memorize and retrieve the information patterns correctly and efficiently with high retrieval accuracy.
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30
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Ristič D, Gosak M. Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons. Front Comput Neurosci 2022; 16:885720. [PMID: 35521427 PMCID: PMC9062746 DOI: 10.3389/fncom.2022.885720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.
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Affiliation(s)
- David Ristič
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Marko Gosak
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
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31
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Learning-induced biases in the ongoing dynamics of sensory representations predict stimulus generalization. Cell Rep 2022; 38:110340. [PMID: 35139386 DOI: 10.1016/j.celrep.2022.110340] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/16/2021] [Accepted: 01/14/2022] [Indexed: 11/22/2022] Open
Abstract
Sensory stimuli have long been thought to be represented in the brain as activity patterns of specific neuronal assemblies. However, we still know relatively little about the long-term dynamics of sensory representations. Using chronic in vivo calcium imaging in the mouse auditory cortex, we find that sensory representations undergo continuous recombination, even under behaviorally stable conditions. Auditory cued fear conditioning introduces a bias into these ongoing dynamics, resulting in a long-lasting increase in the number of stimuli activating the same subset of neurons. This plasticity is specific for stimuli sharing representational similarity to the conditioned sound prior to conditioning and predicts behaviorally observed stimulus generalization. Our findings demonstrate that learning-induced plasticity leading to a representational linkage between the conditioned stimulus and non-conditioned stimuli weaves into ongoing dynamics of the brain rather than acting on an otherwise static substrate.
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32
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Bi H, di Volo M, Torcini A. Asynchronous and Coherent Dynamics in Balanced Excitatory-Inhibitory Spiking Networks. Front Syst Neurosci 2021; 15:752261. [PMID: 34955768 PMCID: PMC8702645 DOI: 10.3389/fnsys.2021.752261] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 10/27/2021] [Indexed: 01/14/2023] Open
Abstract
Dynamic excitatory-inhibitory (E-I) balance is a paradigmatic mechanism invoked to explain the irregular low firing activity observed in the cortex. However, we will show that the E-I balance can be at the origin of other regimes observable in the brain. The analysis is performed by combining extensive simulations of sparse E-I networks composed of N spiking neurons with analytical investigations of low dimensional neural mass models. The bifurcation diagrams, derived for the neural mass model, allow us to classify the possible asynchronous and coherent behaviors emerging in balanced E-I networks with structural heterogeneity for any finite in-degree K. Analytic mean-field (MF) results show that both supra and sub-threshold balanced asynchronous regimes are observable in our system in the limit N >> K >> 1. Due to the heterogeneity, the asynchronous states are characterized at the microscopic level by the splitting of the neurons in to three groups: silent, fluctuation, and mean driven. These features are consistent with experimental observations reported for heterogeneous neural circuits. The coherent rhythms observed in our system can range from periodic and quasi-periodic collective oscillations (COs) to coherent chaos. These rhythms are characterized by regular or irregular temporal fluctuations joined to spatial coherence somehow similar to coherent fluctuations observed in the cortex over multiple spatial scales. The COs can emerge due to two different mechanisms. A first mechanism analogous to the pyramidal-interneuron gamma (PING), usually invoked for the emergence of γ-oscillations. The second mechanism is intimately related to the presence of current fluctuations, which sustain COs characterized by an essentially simultaneous bursting of the two populations. We observe period-doubling cascades involving the PING-like COs finally leading to the appearance of coherent chaos. Fluctuation driven COs are usually observable in our system as quasi-periodic collective motions characterized by two incommensurate frequencies. However, for sufficiently strong current fluctuations these collective rhythms can lock. This represents a novel mechanism of frequency locking in neural populations promoted by intrinsic fluctuations. COs are observable for any finite in-degree K, however, their existence in the limit N >> K >> 1 appears as uncertain.
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Affiliation(s)
- Hongjie Bi
- CY Cergy Paris Université, Laboratoire de Physique Théorique et Modélisation, CNRS, UMR 8089, Cergy-Pontoise, France
- Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology, Okinawa, Japan
| | - Matteo di Volo
- CY Cergy Paris Université, Laboratoire de Physique Théorique et Modélisation, CNRS, UMR 8089, Cergy-Pontoise, France
| | - Alessandro Torcini
- CY Cergy Paris Université, Laboratoire de Physique Théorique et Modélisation, CNRS, UMR 8089, Cergy-Pontoise, France
- CNR-Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
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33
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Larisch R, Gönner L, Teichmann M, Hamker FH. Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity. PLoS Comput Biol 2021; 17:e1009566. [PMID: 34843455 PMCID: PMC8629393 DOI: 10.1371/journal.pcbi.1009566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/15/2021] [Indexed: 11/18/2022] Open
Abstract
Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network. Synaptic plasticity is crucial for the development of efficient input representation in the different sensory cortices, such as the primary visual cortex. Efficient visual representation is determined by two factors: representational efficiency, i.e. how many different input features can be represented, and metabolic efficiency, i.e. how many spikes are required to represent a specific feature. Previous research has pointed out the importance of plasticity at excitatory synapses to achieve high representational efficiency and feedback inhibition as a gain control mechanism for controlling metabolic efficiency. However, it is only partially understood how the influence of inhibitory plasticity on excitatory plasticity can lead to an efficient representation. Using a spiking neural network, we show that plasticity at feed-forward and feedback inhibitory synapses is necessary for the emergence of well-distributed neuronal selectivity to improve representational efficiency. Further, the emergent balance between excitatory and inhibitory currents improves the metabolic efficiency, and leads to contrast-invariant tuning as an inherent network property. Extending previous work, our simulation results highlight the importance of plasticity at inhibitory synapses.
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Affiliation(s)
- René Larisch
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- * E-mail: (RL); (FHH)
| | - Lorenz Gönner
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Faculty of Psychology, Lifespan Developmental Neuroscience, TU Dresden, Dresden, Germany
| | - Michael Teichmann
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
| | - Fred H. Hamker
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Bernstein Center Computational Neuroscience, Berlin, Germany
- * E-mail: (RL); (FHH)
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34
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Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation. Proc Natl Acad Sci U S A 2021; 118:2023832118. [PMID: 34772802 DOI: 10.1073/pnas.2023832118] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2021] [Indexed: 11/18/2022] Open
Abstract
Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity and spontaneous synaptic turnover induce neuron exchange. The gradual exchange allows activity-dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs, and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.
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35
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Bruña R, Maestú F, López-Sanz D, Bagic A, Cohen AD, Chang YF, Cheng Y, Doman J, Huppert T, Kim T, Roush RE, Snitz BE, Becker JT. Sex Differences in Magnetoencephalography-Identified Functional Connectivity in the Human Connectome Project Connectomics of Brain Aging and Dementia Cohort. Brain Connect 2021; 12:561-570. [PMID: 34726478 PMCID: PMC9419974 DOI: 10.1089/brain.2021.0059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: The human brain shows modest traits of sexual dimorphism, with the female brain, on average, 10% smaller than the male brain. These differences do not imply a lowered cognitive performance, but suggest a more optimal brain organization in women. Here we evaluate the patterns of functional connectivity (FC) in women and men from the Connectomics of Brain Aging and Dementia sample. Methods: We used phase locking values to calculate FC from the magnetoencephalography time series in a sample of 138 old adults (87 females and 51 males). We compared the FC patterns between sexes, with the intention of detecting regions with different levels of connectivity. Results: We found a frontal cluster, involving anterior cingulate and the medial frontal lobe, where women showed higher FC values than men. Involved connections included the following: (1) medial parietal areas, such as posterior cingulate cortices and precunei; (2) right insula; and (3) medium cingulate and paracingulate cortices. Moreover, these differences persisted when considering only cognitively intact individuals, but not when considering only cognitively impaired individuals. Discussion: Increased anteroposterior FC has been identified as a biomarker for increased risk of developing cognitive impairment or dementia. In our study, cognitively intact women showed higher levels of FC than their male counterparts. This result suggests that neurodegenerative processes could be taking place in these women, but the changes are undetected by current diagnosis tools. FC, as measured here, might be valuable for early identification of this neurodegeneration.
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Affiliation(s)
- Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Psychobiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Anto Bagic
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Statistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ann D Cohen
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Yue-Fang Chang
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Yu Cheng
- Department of Statistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Biostatistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jack Doman
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ted Huppert
- Department of Electrical Engineering, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tae Kim
- Department of Radiology, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Rebecca E Roush
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Beth E Snitz
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - James T Becker
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurology, and The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Psychology, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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36
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Sherf N, Shamir M. STDP and the distribution of preferred phases in the whisker system. PLoS Comput Biol 2021; 17:e1009353. [PMID: 34534208 PMCID: PMC8480728 DOI: 10.1371/journal.pcbi.1009353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 09/29/2021] [Accepted: 08/17/2021] [Indexed: 11/19/2022] Open
Abstract
Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker’s position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory. The distribution of preferred phases of whisking neurons in the somatosensory system of rats and mice presents a conundrum: a simple pooling model predicts a distribution that is an order of magnitude narrower than what is observed empirically. Here, we suggest that this non-trivial distribution may result from activity-dependent plasticity in the form of spike timing dependent plasticity (STDP). We show that under STDP, the synaptic weights do not converge to a fixed value, but rather remain dynamic. As a result, the preferred phases of the whisking neurons vary in time, hence inducing a non-trivial distribution of preferred phases, which is governed by the STDP rule. Our results imply that the considerable synaptic volatility which has long been viewed as a difficulty that needs to be overcome, may actually be an underlying principle of the organization of the central nervous system.
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Affiliation(s)
- Nimrod Sherf
- Physics Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- * E-mail:
| | - Maoz Shamir
- Physics Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Physiology and Cell Biology Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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37
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Raman DV, O'Leary T. Optimal plasticity for memory maintenance during ongoing synaptic change. eLife 2021; 10:62912. [PMID: 34519270 PMCID: PMC8504970 DOI: 10.7554/elife.62912] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Synaptic connections in many brain circuits fluctuate, exhibiting substantial turnover and remodelling over hours to days. Surprisingly, experiments show that most of this flux in connectivity persists in the absence of learning or known plasticity signals. How can neural circuits retain learned information despite a large proportion of ongoing and potentially disruptive synaptic changes? We address this question from first principles by analysing how much compensatory plasticity would be required to optimally counteract ongoing fluctuations, regardless of whether fluctuations are random or systematic. Remarkably, we find that the answer is largely independent of plasticity mechanisms and circuit architectures: compensatory plasticity should be at most equal in magnitude to fluctuations, and often less, in direct agreement with previously unexplained experimental observations. Moreover, our analysis shows that a high proportion of learning-independent synaptic change is consistent with plasticity mechanisms that accurately compute error gradients.
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Affiliation(s)
- Dhruva V Raman
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Timothy O'Leary
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
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38
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Hurwitz C, Kudryashova N, Onken A, Hennig MH. Building population models for large-scale neural recordings: Opportunities and pitfalls. Curr Opin Neurobiol 2021; 70:64-73. [PMID: 34411907 DOI: 10.1016/j.conb.2021.07.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/11/2021] [Accepted: 07/14/2021] [Indexed: 11/15/2022]
Abstract
Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.
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Affiliation(s)
- Cole Hurwitz
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom
| | - Nina Kudryashova
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom
| | - Arno Onken
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom
| | - Matthias H Hennig
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom.
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39
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Salaj D, Subramoney A, Kraisnikovic C, Bellec G, Legenstein R, Maass W. Spike frequency adaptation supports network computations on temporally dispersed information. eLife 2021; 10:e65459. [PMID: 34310281 PMCID: PMC8313230 DOI: 10.7554/elife.65459] [Citation(s) in RCA: 15] [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: 12/04/2020] [Accepted: 06/29/2021] [Indexed: 11/13/2022] Open
Abstract
For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well-known property of a substantial fraction of neurons in the neocortex - especially in higher areas of the human neocortex - moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.
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Affiliation(s)
- Darjan Salaj
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Anand Subramoney
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Ceca Kraisnikovic
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Guillaume Bellec
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
- Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Robert Legenstein
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Wolfgang Maass
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
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40
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Computational roles of intrinsic synaptic dynamics. Curr Opin Neurobiol 2021; 70:34-42. [PMID: 34303124 DOI: 10.1016/j.conb.2021.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/14/2021] [Accepted: 06/15/2021] [Indexed: 12/26/2022]
Abstract
Conventional theories assume that long-term information storage in the brain is implemented by modifying synaptic efficacy. Recent experimental findings challenge this view by demonstrating that dendritic spine sizes, or their corresponding synaptic weights, are highly volatile even in the absence of neural activity. Here, we review previous computational works on the roles of these intrinsic synaptic dynamics. We first present the possibility for neuronal networks to sustain stable performance in their presence, and we then hypothesize that intrinsic dynamics could be more than mere noise to withstand, but they may improve information processing in the brain.
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41
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Aljadeff J, Gillett M, Pereira Obilinovic U, Brunel N. From synapse to network: models of information storage and retrieval in neural circuits. Curr Opin Neurobiol 2021; 70:24-33. [PMID: 34175521 DOI: 10.1016/j.conb.2021.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/06/2021] [Accepted: 05/25/2021] [Indexed: 10/21/2022]
Abstract
The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this information and that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental data and in understanding how plasticity rules affect the dynamics of recurrent networks. We show that the dynamics generated by such networks exhibit a large degree of diversity, depending on parameters, similar to experimental observations in vivo during delayed response tasks.
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Affiliation(s)
- Johnatan Aljadeff
- Neurobiology Section, Division of Biological Sciences, UC San Diego, USA
| | | | | | - Nicolas Brunel
- Department of Neurobiology, Duke University, USA; Department of Physics, Duke University, USA.
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42
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Inhibitory control in neuronal networks relies on the extracellular matrix integrity. Cell Mol Life Sci 2021; 78:5647-5663. [PMID: 34128077 PMCID: PMC8257544 DOI: 10.1007/s00018-021-03861-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/11/2021] [Accepted: 05/19/2021] [Indexed: 11/14/2022]
Abstract
Inhibitory control is essential for the regulation of neuronal network activity, where excitatory and inhibitory synapses can act synergistically, reciprocally, and antagonistically. Sustained excitation-inhibition (E-I) balance, therefore, relies on the orchestrated adjustment of excitatory and inhibitory synaptic strength. While growing evidence indicates that the brain’s extracellular matrix (ECM) is a crucial regulator of excitatory synapse plasticity, it remains unclear whether and how the ECM contributes to inhibitory control in neuronal networks. Here we studied the simultaneous changes in excitatory and inhibitory connectivity after ECM depletion. We demonstrate that the ECM supports the maintenance of E-I balance by retaining inhibitory connectivity. Quantification of synapses and super-resolution microscopy showed that depletion of the ECM in mature neuronal networks preferentially decreases the density of inhibitory synapses and the size of individual inhibitory postsynaptic scaffolds. The reduction of inhibitory synapse density is partially compensated by the homeostatically increasing synaptic strength via the reduction of presynaptic GABAB receptors, as indicated by patch-clamp measurements and GABAB receptor expression quantifications. However, both spiking and bursting activity in neuronal networks is increased after ECM depletion, as indicated by multi-electrode recordings. With computational modelling, we determined that ECM depletion reduces the inhibitory connectivity to an extent that the inhibitory synapse scaling does not fully compensate for the reduced inhibitory synapse density. Our results indicate that the brain’s ECM preserves the balanced state of neuronal networks by supporting inhibitory control via inhibitory synapse stabilization, which expands the current understanding of brain activity regulation. ![]()
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43
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Giorgi C, Marinelli S. Roles and Transcriptional Responses of Inhibitory Neurons in Learning and Memory. Front Mol Neurosci 2021; 14:689952. [PMID: 34211369 PMCID: PMC8239217 DOI: 10.3389/fnmol.2021.689952] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/18/2021] [Indexed: 12/26/2022] Open
Abstract
Increasing evidence supports a model whereby memories are encoded by sparse ensembles of neurons called engrams, activated during memory encoding and reactivated upon recall. An engram consists of a network of cells that undergo long-lasting modifications of their transcriptional programs and connectivity. Ground-breaking advancements in this field have been made possible by the creative exploitation of the characteristic transcriptional responses of neurons to activity, allowing both engram labeling and manipulation. Nevertheless, numerous aspects of engram cell-type composition and function remain to be addressed. As recent transcriptomic studies have revealed, memory encoding induces persistent transcriptional and functional changes in a plethora of neuronal subtypes and non-neuronal cells, including glutamatergic excitatory neurons, GABAergic inhibitory neurons, and glia cells. Dissecting the contribution of these different cellular classes to memory engram formation and activity is quite a challenging yet essential endeavor. In this review, we focus on the role played by the GABAergic inhibitory component of the engram through two complementary lenses. On one hand, we report on available physiological evidence addressing the involvement of inhibitory neurons to different stages of memory formation, consolidation, storage and recall. On the other, we capitalize on a growing number of transcriptomic studies that profile the transcriptional response of inhibitory neurons to activity, revealing important clues on their potential involvement in learning and memory processes. The picture that emerges suggests that inhibitory neurons are an essential component of the engram, likely involved in engram allocation, in tuning engram excitation and in storing the memory trace.
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Affiliation(s)
- Corinna Giorgi
- CNR, Institute of Molecular Biology and Pathology, Rome, Italy.,European Brain Research Institute (EBRI), Fondazione Rita Levi-Montalcini, Rome, Italy
| | - Silvia Marinelli
- European Brain Research Institute (EBRI), Fondazione Rita Levi-Montalcini, Rome, Italy
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44
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Kasai H, Ziv NE, Okazaki H, Yagishita S, Toyoizumi T. Spine dynamics in the brain, mental disorders and artificial neural networks. Nat Rev Neurosci 2021; 22:407-422. [PMID: 34050339 DOI: 10.1038/s41583-021-00467-3] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2021] [Indexed: 12/15/2022]
Abstract
In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain's ability to learn from experience and cope with new challenges. Importantly, they exhibit structural dynamics that depend on activity, excitatory input and inhibitory input (synaptic plasticity or 'extrinsic' dynamics) and dynamics independent of activity ('intrinsic' dynamics), both of which are subject to neuromodulatory influences and reinforcers such as dopamine. Here we succinctly review extrinsic and intrinsic dynamics, compare these with parallels in machine learning where they exist, describe the importance of intrinsic dynamics for memory management and adaptation, and speculate on how disruption of extrinsic and intrinsic dynamics may give rise to mental disorders. Throughout, we also highlight algorithmic features of spine dynamics that may be relevant to future artificial intelligence developments.
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Affiliation(s)
- Haruo Kasai
- Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan. .,International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
| | - Noam E Ziv
- Technion Faculty of Medicine and Network Biology Research Labs, Technion City, Haifa, Israel
| | - Hitoshi Okazaki
- Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.,International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Sho Yagishita
- Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.,International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama, Japan.,Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
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45
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Akil AE, Rosenbaum R, Josić K. Balanced networks under spike-time dependent plasticity. PLoS Comput Biol 2021; 17:e1008958. [PMID: 33979336 PMCID: PMC8143429 DOI: 10.1371/journal.pcbi.1008958] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 05/24/2021] [Accepted: 04/12/2021] [Indexed: 11/28/2022] Open
Abstract
The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input. Animals are able to learn complex tasks through changes in individual synapses between cells. Such changes lead to the coevolution of neural activity patterns and the structure of neural connectivity, but the consequences of these interactions are not fully understood. We consider plasticity in model neural networks which achieve an average balance between the excitatory and inhibitory synaptic inputs to different cells, and display cortical–like, irregular activity. We extend the theory of balanced networks to account for synaptic plasticity and show which rules can maintain balance, and which will drive the network into a different state. This theory of plasticity can provide insights into the relationship between stimuli, network dynamics, and synaptic circuitry.
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Affiliation(s)
- Alan Eric Akil
- Department of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, United States of America
- Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas, United States of America
- Department of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America
- * E-mail:
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46
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Inhibitory neurons exhibit high controlling ability in the cortical microconnectome. PLoS Comput Biol 2021; 17:e1008846. [PMID: 33831009 PMCID: PMC8031186 DOI: 10.1371/journal.pcbi.1008846] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 03/01/2021] [Indexed: 02/08/2023] Open
Abstract
The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states. How small numbers of inhibitory neurons functionally keep balance with large numbers of excitatory neurons in the brain by controlling each other is a fundamental question. Especially, this study quantitatively evaluated a topological mechanism of interaction networks in terms of controlling abilities of individual cortical neurons to other neurons. Combination of simultaneous electrical recording of ~1000 neurons and a quantitative evaluation method of neuronal interactions including excitatory-inhibitory categories, enabled us to evaluate the influence of individual neurons not only about firing rate but also about their relative positions in the networks and controllable ability of other neurons. Especially, the result showed that inhibitory neurons have more controlling ability than excitatory neurons, and such neurons were more often observed in deep layers. Because the limited number of neurons in terms controlling ability were much smaller than neurons based on centrality measure and, of course, more directly selected neurons based on their ability to control other neurons, the selection method of important neurons will help not only to produce realistic computational models but also will help to stimulate brain to effectively treat imbalanced disease states.
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Abstract
The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and \documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective \documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.
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48
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Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers. Proc Natl Acad Sci U S A 2021; 118:2018459118. [PMID: 33723048 DOI: 10.1073/pnas.2018459118] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
The interplay between excitation and inhibition is crucial for neuronal circuitry in the brain. Inhibitory cell fractions in the neocortex and hippocampus are typically maintained at 15 to 30%, which is assumed to be important for stable dynamics. We have studied systematically the role of precisely controlled excitatory/inhibitory (E/I) cellular ratios on network activity using mice hippocampal cultures. Surprisingly, networks with varying E/I ratios maintain stable bursting dynamics. Interburst intervals remain constant for most ratios, except in the extremes of 0 to 10% and 90 to 100% inhibitory cells. Single-cell recordings and modeling suggest that networks adapt to chronic alterations of E/I compositions by balancing E/I connectivity. Gradual blockade of inhibition substantiates the agreement between the model and experiment and defines its limits. Combining measurements of population and single-cell activity with theoretical modeling, we provide a clearer picture of how E/I balance is preserved and where it fails in living neuronal networks.
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49
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Wason TD. A model integrating multiple processes of synchronization and coherence for information instantiation within a cortical area. Biosystems 2021; 205:104403. [PMID: 33746019 DOI: 10.1016/j.biosystems.2021.104403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 03/05/2021] [Indexed: 12/14/2022]
Abstract
What is the form of dynamic, e.g., sensory, information in the mammalian cortex? Information in the cortex is modeled as a coherence map of a mixed chimera state of synchronous, phasic, and disordered minicolumns. The theoretical model is built on neurophysiological evidence. Complex spatiotemporal information is instantiated through a system of interacting biological processes that generate a synchronized cortical area, a coherent aperture. Minicolumn elements are grouped in macrocolumns in an array analogous to a phased-array radar, modeled as an aperture, a "hole through which radiant energy flows." Coherence maps in a cortical area transform inputs from multiple sources into outputs to multiple targets, while reducing complexity and entropy. Coherent apertures can assume extremely large numbers of different information states as coherence maps, which can be communicated among apertures with corresponding very large bandwidths. The coherent aperture model incorporates considerable reported research, integrating five conceptually and mathematically independent processes: 1) a damped Kuramoto network model, 2) a pumped area field potential, 3) the gating of nearly coincident spikes, 4) the coherence of activity across cortical lamina, and 5) complex information formed through functions in macrocolumns. Biological processes and their interactions are described in equations and a functional circuit such that the mathematical pieces can be assembled the same way the neurophysiological ones are. The model can be conceptually convolved over the specifics of local cortical areas within and across species. A coherent aperture becomes a node in a graph of cortical areas with a corresponding distribution of information.
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Affiliation(s)
- Thomas D Wason
- North Carolina State University, Department of Biological Sciences, Meitzen Laboratory, Campus Box 7617, 128 David Clark Labs, Raleigh, NC 27695-7617, USA.
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50
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Modulation of Coordinated Activity across Cortical Layers by Plasticity of Inhibitory Synapses. Cell Rep 2021; 30:630-641.e5. [PMID: 31968242 PMCID: PMC6988114 DOI: 10.1016/j.celrep.2019.12.052] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 11/21/2019] [Accepted: 12/13/2019] [Indexed: 11/25/2022] Open
Abstract
In the neocortex, synaptic inhibition shapes all forms of spontaneous and sensory evoked activity. Importantly, inhibitory transmission is highly plastic, but the functional role of inhibitory synaptic plasticity is unknown. In the mouse barrel cortex, activation of layer (L) 2/3 pyramidal neurons (PNs) elicits strong feedforward inhibition (FFI) onto L5 PNs. We find that FFI involving parvalbumin (PV)-expressing cells is strongly potentiated by postsynaptic PN burst firing. FFI plasticity modifies the PN excitation-to-inhibition (E/I) ratio, strongly modulates PN gain, and alters information transfer across cortical layers. Moreover, our LTPi-inducing protocol modifies firing of L5 PNs and alters the temporal association of PN spikes to γ-oscillations both in vitro and in vivo. All of these effects are captured by unbalancing the E/I ratio in a feedforward inhibition circuit model. Altogether, our results indicate that activity-dependent modulation of perisomatic inhibitory strength effectively influences the participation of single principal cortical neurons to cognition-relevant network activity. Feedforward inhibition (FFI) of layer 5 pyramidal neurons (PNs) is highly plastic Long-term potentiation of FFI modulates spiking activity of layer 5 PNs LTPi affects information transfer across cortical layers The strength of LTPi determines the phase locking of PN firing to γ-oscillations
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