1
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Villalobos N. Disinhibition Is an Essential Network Motif Coordinated by GABA Levels and GABA B Receptors. Int J Mol Sci 2024; 25:1340. [PMID: 38279339 PMCID: PMC10816949 DOI: 10.3390/ijms25021340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 01/28/2024] Open
Abstract
Network dynamics are crucial for action and sensation. Changes in synaptic physiology lead to the reorganization of local microcircuits. Consequently, the functional state of the network impacts the output signal depending on the firing patterns of its units. Networks exhibit steady states in which neurons show various activities, producing many networks with diverse properties. Transitions between network states determine the output signal generated and its functional results. The temporal dynamics of excitation/inhibition allow a shift between states in an operational network. Therefore, a process capable of modulating the dynamics of excitation/inhibition may be functionally important. This process is known as disinhibition. In this review, we describe the effect of GABA levels and GABAB receptors on tonic inhibition, which causes changes (due to disinhibition) in network dynamics, leading to synchronous functional oscillations.
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Affiliation(s)
- Nelson Villalobos
- Academia de Fisiología, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón, Colonia Casco de Santo Tomás, Ciudad de México 11340, Mexico;
- Sección de Estudios Posgrado e Investigación de la Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón, Colonia Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico
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2
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Gast R, Solla SA, Kennedy A. Neural heterogeneity controls computations in spiking neural networks. Proc Natl Acad Sci U S A 2024; 121:e2311885121. [PMID: 38198531 PMCID: PMC10801870 DOI: 10.1073/pnas.2311885121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/27/2023] [Indexed: 01/12/2024] Open
Abstract
The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.
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Affiliation(s)
- Richard Gast
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL60611
- Aligning Science Across Parkinson’s Collaborative Research Network, Chevy Chase, MD20815
| | - Sara A. Solla
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL60611
| | - Ann Kennedy
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL60611
- Aligning Science Across Parkinson’s Collaborative Research Network, Chevy Chase, MD20815
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3
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Si H, Sun X. Inter-areal transmission of multiple neural signals through frequency-division-multiplexing communication. Cogn Neurodyn 2023; 17:1153-1165. [PMID: 37786658 PMCID: PMC10542065 DOI: 10.1007/s11571-022-09914-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 10/26/2022] [Accepted: 11/17/2022] [Indexed: 12/03/2022] Open
Abstract
Inter-areal information transmission in the brain cortex relates to cognitive functions. Researches used to pay attention to activity pattern transmission, signals gating, or routing in neuronal networks. However, the underlying mechanism of simultaneous transmission of multiple neural signals in the same channel across networks remains unclear. In this work, we construct a two-layer feedforward neuronal network (sender-receiver) with each layer's intrinsic rhythms consisting of slow- (low-frequency) and fast- gamma rhythms (high-frequency), investigating how to realize simultaneous transmission of multiple signals in neuronal systems. With the aid of resonance and frequency analysis, it is shown that low- and high-frequency signals can be transmitted simultaneously in such a feedforward network through frequency division multiplexing (FDM) communication. The transmission performance is related to the local resonance, connectivity, as well as background noise. Moreover, low- and high-frequency signals can also be gated or selected with appropriate adjustments of recurrent connection strength and delay, and background noise. Our model might provide a novel insight into the underlying mechanism of complex signals communication between different cortex areas.
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Affiliation(s)
- Hao Si
- School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876 China
| | - Xiaojuan Sun
- School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876 China
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4
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Gehr C, Sibille J, Kremkow J. Retinal input integration in excitatory and inhibitory neurons in the mouse superior colliculus in vivo. eLife 2023; 12:RP88289. [PMID: 37682267 PMCID: PMC10491433 DOI: 10.7554/elife.88289] [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] [Indexed: 09/09/2023] Open
Abstract
The superior colliculus (SC) is a midbrain structure that receives inputs from retinal ganglion cells (RGCs). The SC contains one of the highest densities of inhibitory neurons in the brain but whether excitatory and inhibitory SC neurons differentially integrate retinal activity in vivo is still largely unknown. We recently established a recording approach to measure the activity of RGCs simultaneously with their postsynaptic SC targets in vivo, to study how SC neurons integrate RGC activity. Here, we employ this method to investigate the functional properties that govern retinocollicular signaling in a cell type-specific manner by identifying GABAergic SC neurons using optotagging in VGAT-ChR2 mice. Our results demonstrate that both excitatory and inhibitory SC neurons receive comparably strong RGC inputs and similar wiring rules apply for RGCs innervation of both SC cell types, unlike the cell type-specific connectivity in the thalamocortical system. Moreover, retinal activity contributed more to the spiking activity of postsynaptic excitatory compared to inhibitory SC neurons. This study deepens our understanding of cell type-specific retinocollicular functional connectivity and emphasizes that the two major brain areas for visual processing, the visual cortex and the SC, differently integrate sensory afferent inputs.
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Affiliation(s)
- Carolin Gehr
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jeremie Sibille
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jens Kremkow
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
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5
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Guo L, Kumar A. Role of interneuron subtypes in controlling trial-by-trial output variability in the neocortex. Commun Biol 2023; 6:874. [PMID: 37620550 PMCID: PMC10449833 DOI: 10.1038/s42003-023-05231-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo which shapes the stimulus response. Computational models have revealed how local network structure and feedforward inputs shape the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in this process is less understood. To address this, we investigate the dynamics of stimulus response in a cortical microcircuit model with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). Our findings demonstrate that the balance of inputs to different neuron populations and input covariances are the primary determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input balances. In general, the network exhibits smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Importantly, our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.
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Affiliation(s)
- Lihao Guo
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, Stockholm, Sweden.
- Scilife Lab, Stockholm, Sweden.
| | - Arvind Kumar
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, Stockholm, Sweden.
- Scilife Lab, Stockholm, Sweden.
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6
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Abstract
Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.
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Affiliation(s)
- Gouki Okazawa
- Center for Neural Science, New York University, New York, NY, USA;
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY, USA;
- Department of Psychology, New York University, New York, NY, USA
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7
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Riquelme JL, Hemberger M, Laurent G, Gjorgjieva J. Single spikes drive sequential propagation and routing of activity in a cortical network. eLife 2023; 12:79928. [PMID: 36780217 PMCID: PMC9925052 DOI: 10.7554/elife.79928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 12/19/2022] [Indexed: 02/14/2023] Open
Abstract
Single spikes can trigger repeatable firing sequences in cortical networks. The mechanisms that support reliable propagation of activity from such small events and their functional consequences remain unclear. By constraining a recurrent network model with experimental statistics from turtle cortex, we generate reliable and temporally precise sequences from single spike triggers. We find that rare strong connections support sequence propagation, while dense weak connections modulate propagation reliability. We identify sections of sequences corresponding to divergent branches of strongly connected neurons which can be selectively gated. Applying external inputs to specific neurons in the sparse backbone of strong connections can effectively control propagation and route activity within the network. Finally, we demonstrate that concurrent sequences interact reliably, generating a highly combinatorial space of sequence activations. Our results reveal the impact of individual spikes in cortical circuits, detailing how repeatable sequences of activity can be triggered, sustained, and controlled during cortical computations.
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Affiliation(s)
- Juan Luis Riquelme
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany,School of Life Sciences, Technical University of MunichFreisingGermany
| | - Mike Hemberger
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany
| | - Gilles Laurent
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany
| | - Julijana Gjorgjieva
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany,School of Life Sciences, Technical University of MunichFreisingGermany
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8
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Developmental depression-to-facilitation shift controls excitation-inhibition balance. Commun Biol 2022; 5:873. [PMID: 36008708 PMCID: PMC9411206 DOI: 10.1038/s42003-022-03801-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/04/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modeling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of short-lasting memory traces governed by short-term plasticity development. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences. Using computational modelling this study proposes that the commonly observed depression-to-facilitation shift across development controls excitation-inhibition balance in the brain.
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9
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Inagaki HK, Chen S, Daie K, Finkelstein A, Fontolan L, Romani S, Svoboda K. Neural Algorithms and Circuits for Motor Planning. Annu Rev Neurosci 2022; 45:249-271. [PMID: 35316610 DOI: 10.1146/annurev-neuro-092021-121730] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.
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Affiliation(s)
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Kayvon Daie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
| | - Arseny Finkelstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Lorenzo Fontolan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
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10
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Abstract
Breathing is a vital rhythmic motor behavior with a surprisingly broad influence on the brain and body. The apparent simplicity of breathing belies a complex neural control system, the breathing central pattern generator (bCPG), that exhibits diverse operational modes to regulate gas exchange and coordinate breathing with an array of behaviors. In this review, we focus on selected advances in our understanding of the bCPG. At the core of the bCPG is the preBötzinger complex (preBötC), which drives inspiratory rhythm via an unexpectedly sophisticated emergent mechanism. Synchronization dynamics underlying preBötC rhythmogenesis imbue the system with robustness and lability. These dynamics are modulated by inputs from throughout the brain and generate rhythmic, patterned activity that is widely distributed. The connectivity and an emerging literature support a link between breathing, emotion, and cognition that is becoming experimentally tractable. These advances bring great potential for elucidating function and dysfunction in breathing and other mammalian neural circuits.
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Affiliation(s)
- Sufyan Ashhad
- Department of Neurobiology, University of California at Los Angeles, Los Angeles, California, USA;
| | - Kaiwen Kam
- Department of Cell Biology and Anatomy, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, USA
| | | | - Jack L Feldman
- Department of Neurobiology, University of California at Los Angeles, Los Angeles, California, USA;
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11
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A feedforward inhibitory premotor circuit for auditory-vocal interactions in zebra finches. Proc Natl Acad Sci U S A 2022; 119:e2118448119. [PMID: 35658073 PMCID: PMC9191632 DOI: 10.1073/pnas.2118448119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Significance During conversations, we frequently alternate between listening and speaking. This involves withholding responses while the other person is vocalizing and rapidly initiating a reply once they stop. Similar exchanges also occur in other animals, such as songbirds, yet little is known about how brain areas responsible for vocal production are influenced by areas dedicated to listening. Here, we combined neural recordings and mathematical modeling of a sensorimotor circuit to show that input-dependent inhibition can both suppress vocal responses and regulate the onset latencies of vocalizations. Our resulting model provides a simple generalizable circuit mechanism by which inhibition precisely times vocal output and integrates auditory input within a premotor nucleus.
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12
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Braun W, Memmesheimer RM. High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1. PLoS Comput Biol 2022; 18:e1009891. [PMID: 35176028 PMCID: PMC8890743 DOI: 10.1371/journal.pcbi.1009891] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 03/02/2022] [Accepted: 02/02/2022] [Indexed: 11/19/2022] Open
Abstract
Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.
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Affiliation(s)
- Wilhelm Braun
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail: (WB); (R-MM)
| | - Raoul-Martin Memmesheimer
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- * E-mail: (WB); (R-MM)
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13
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Cao AS, Van Hooser SD. Paired Feed-Forward Excitation With Delayed Inhibition Allows High Frequency Computations Across Brain Regions. Front Neural Circuits 2022; 15:803065. [PMID: 35210993 PMCID: PMC8862685 DOI: 10.3389/fncir.2021.803065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/29/2021] [Indexed: 11/30/2022] Open
Abstract
The transmission of high frequency temporal information across brain regions is critical to perception, but the mechanisms underlying such transmission remain unclear. Long-range projection patterns across brain areas are often comprised of paired feed-forward excitation followed closely by delayed inhibition, including the thalamic triad synapse, thalamic projections to cortex, and projections within the hippocampus. Previous studies have shown that these joint projections produce a shortened period of depolarization, sharpening the timing window over which the postsynaptic neuron can fire. Here we show that these projections can facilitate the transmission of high frequency computations even at frequencies that are highly filtered by neuronal membranes. This temporal facilitation occurred over a range of synaptic parameter values, including variations in synaptic strength, synaptic time constants, short-term synaptic depression, and the delay between excitation and inhibition. Further, these projections can coordinate computations across multiple network levels, even amid ongoing local activity. We suggest that paired feed-forward excitation and inhibition provide a hybrid signal-carrying both a value and a clock-like trigger-to allow circuits to be responsive to input whenever it arrives.
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Affiliation(s)
- Alexandra S. Cao
- Department of Biology, Brandeis University, Waltham, MA, United States
- Volen Center for Complex Systems, Brandeis University, Waltham, MA, United States
| | - Stephen D. Van Hooser
- Department of Biology, Brandeis University, Waltham, MA, United States
- Volen Center for Complex Systems, Brandeis University, Waltham, MA, United States
- Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University, Waltham, MA, United States
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14
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Bryson A, Berkovic SF, Petrou S, Grayden DB. State transitions through inhibitory interneurons in a cortical network model. PLoS Comput Biol 2021; 17:e1009521. [PMID: 34653178 PMCID: PMC8550371 DOI: 10.1371/journal.pcbi.1009521] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 10/27/2021] [Accepted: 10/04/2021] [Indexed: 11/18/2022] Open
Abstract
Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state. Inhibitory interneurons comprise a significant proportion of all cortical neurons and play a crucial role in sustaining normal neural activity in the brain. Although it is well established that there exist distinct subtypes of interneurons, the impact of different interneuron subtypes upon cortical function remains unclear. In this work, we explore the role of interneuron subtypes for modulating neural activity using a network model containing two of the most common interneuron subtypes. We find that one interneuron subtype, known as fast spiking interneurons, preferentially control the strength of activity between excitatory neurons to regulate changes in network state. These findings suggest that interneuron subtypes may selectively modulate cortical activity to promote different computational capabilities.
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Affiliation(s)
- Alexander Bryson
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia
- Department of Neurology, Austin Health, Heidelberg, Australia
- * E-mail: (AB); (DBG)
| | - Samuel F. Berkovic
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Australia
| | - Steven Petrou
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia
| | - David B. Grayden
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia
- * E-mail: (AB); (DBG)
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15
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Schmuker M, Kupper R, Aertsen A, Wachtler T, Gewaltig MO. Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code. BIOLOGICAL CYBERNETICS 2021; 115:161-176. [PMID: 33787967 DOI: 10.1007/s00422-021-00866-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/31/2021] [Indexed: 06/12/2023]
Abstract
In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.
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Affiliation(s)
- Michael Schmuker
- Honda Research Institute Europe GmbH, Offenbach am Main, Germany.
- Department of Computer Science, Biocomputation Group, University of Hertfordshire, Hatfield, UK.
| | - Rüdiger Kupper
- Honda Research Institute Europe GmbH, Offenbach am Main, Germany
| | - Ad Aertsen
- Bernstein-Center Freiburg and Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
| | - Thomas Wachtler
- Department of Biology II, Ludwig-Maximilians-Universität München, München, Germany
| | - Marc-Oliver Gewaltig
- Honda Research Institute Europe GmbH, Offenbach am Main, Germany
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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16
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Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields. J Neurosci 2020; 40:9634-9649. [PMID: 33168622 PMCID: PMC7726533 DOI: 10.1523/jneurosci.0276-20.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 07/06/2020] [Accepted: 07/12/2020] [Indexed: 12/18/2022] Open
Abstract
Cortical areas comprise multiple types of inhibitory interneurons, with stereotypical connectivity motifs that may follow specific plasticity rules. Yet, their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyze the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Synapses from each inhibitory population change according to distinct plasticity rules. We tested different combinations of three rules: Hebbian, anti-Hebbian, and homeostatic scaling. Depending on the inhibitory plasticity rule, synapses become unspecific (flat), anticorrelated to, or correlated with excitatory synapses. Crucially, the neuron's receptive field (i.e., its response to presynaptic stimuli) depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or nonpreferred inputs, in line with recent experimental findings that show dramatic context-dependent changes of neurons' receptive fields. We thus confirm that a neuron's receptive field does not follow directly from the weight profiles of its presynaptic afferents. Our results show how plasticity rules in various cell types can interact to shape cortical circuit motifs and their dynamics.SIGNIFICANCE STATEMENT Neurons in sensory areas of the cortex are known to respond to specific features of a given input (e.g., specific sound frequencies), but recent experimental studies show that such responses (i.e., their receptive fields) depend on context. Inspired by the cortical connectivity, we built models of excitatory and inhibitory inputs onto a single neuron, to study how receptive fields may change on short and long time scales. We show how various synaptic plasticity rules allow for the emergence of diverse connectivity profiles and, moreover, how their dynamic interaction creates a mechanism by which postsynaptic responses can quickly change. Our work emphasizes multiple roles of inhibition in cortical processing and provides a first mechanistic model for flexible receptive fields.
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Kim CM, Egert U, Kumar A. Dynamics of multiple interacting excitatory and inhibitory populations with delays. Phys Rev E 2020; 102:022308. [PMID: 32942361 DOI: 10.1103/physreve.102.022308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 07/15/2020] [Indexed: 11/07/2022]
Abstract
A network consisting of excitatory and inhibitory (EI) neurons is a canonical model for understanding local cortical network activity. In this study, we extended the local circuit model and investigated how its dynamical landscape can be enriched when it interacts with another excitatory (E) population with long transmission delays. Through analysis of a rate model and numerical simulations of a corresponding network of spiking neurons, we studied the transition from stationary to oscillatory states by analyzing the Hopf bifurcation structure in terms of two network parameters: (1) transmission delay between the EI subnetwork and the E population and (2) inhibitory couplings that induced oscillatory activity in the EI subnetwork. We found that the critical coupling strength can strongly modulate as a function of transmission delay, and consequently the stationary state can be interwoven intricately with the oscillatory state. Such a dynamical landscape gave rise to an isolated stationary state surrounded by multiple oscillatory states that generated different frequency modes, and cross-frequency coupling developed naturally at the bifurcation points. We identified the network motifs with short- and long-range inhibitory connections that underlie the emergence of oscillatory states with multiple frequencies. Thus, we provided a mechanistic explanation of how the transmission delay to and from the additional E population altered the dynamical landscape. In summary, our results demonstrated the potential role of long-range connections in shaping the network activity of local cortical circuits.
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Affiliation(s)
| | - Ulrich Egert
- Bernstein Center Freiburg, 79104 Freiburg, Germany.,Biomicrotechnology, IMTEK-Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany
| | - Arvind Kumar
- Bernstein Center Freiburg, 79104 Freiburg, Germany.,Department of Computational Science and Technology, School for Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Lindstedtsvägen 3, 11428 Stockholm, Sweden
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18
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A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble. ENTROPY 2020; 22:e22080880. [PMID: 33286650 PMCID: PMC7517484 DOI: 10.3390/e22080880] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 08/04/2020] [Accepted: 08/06/2020] [Indexed: 11/17/2022]
Abstract
The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.
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19
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Zhang Y, Zhang X. Portrait of visual cortical circuits for generating neural oscillation dynamics. Cogn Neurodyn 2020; 15:3-16. [PMID: 34109010 DOI: 10.1007/s11571-020-09623-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 07/17/2020] [Accepted: 07/24/2020] [Indexed: 11/30/2022] Open
Abstract
The mouse primary visual cortex (V1) has emerged as a classical system to study neural circuit mechanisms underlying visual function and plasticity. A variety of efferent-afferent neuronal connections exists within the V1 and between the V1 and higher visual cortical areas or thalamic nuclei, indicating that the V1 system is more than a mere receiver in information processing. Sensory representations in the V1 are dynamically correlated with neural activity oscillations that are distributed across different cortical layers in an input-dependent manner. Circuits consisting of excitatory pyramidal cells (PCs) and inhibitory interneurons (INs) are the basis for generating neural oscillations. In general, INs are clustered with their adjacent PCs to form specific microcircuits that gate or filter the neural information. The interaction between these two cell populations has to be coordinated within a local circuit in order to preserve neural coding schemes and maintain excitation-inhibition (E-I) balance. Phasic alternations of the E-I balance can dynamically regulate temporal rhythms of neural oscillation. Accumulating experimental evidence suggests that the two major sub-types of INs, parvalbumin-expressing (PV+) cells and somatostatin-expressing (SOM+) INs, are active in controlling slow and fast oscillations, respectively, in the mouse V1. The review summarizes recent experimental findings on elucidating cellular or circuitry mechanisms for the generation of neural oscillations with distinct rhythms in either developing or matured mouse V1, mainly focusing on visual relaying circuits and distinct local inhibitory circuits.
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Affiliation(s)
- Yuan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Xiaohui Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
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20
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Rezaei H, Aertsen A, Kumar A, Valizadeh A. Facilitating the propagation of spiking activity in feedforward networks by including feedback. PLoS Comput Biol 2020; 16:e1008033. [PMID: 32776924 PMCID: PMC7444537 DOI: 10.1371/journal.pcbi.1008033] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 08/20/2020] [Accepted: 06/08/2020] [Indexed: 01/01/2023] Open
Abstract
Transient oscillations in network activity upon sensory stimulation have been reported in different sensory areas of the brain. These evoked oscillations are the generic response of networks of excitatory and inhibitory neurons (EI-networks) to a transient external input. Recently, it has been shown that this resonance property of EI-networks can be exploited for communication in modular neuronal networks by enabling the transmission of sequences of synchronous spike volleys (’pulse packets’), despite the sparse and weak connectivity between the modules. The condition for successful transmission is that the pulse packet (PP) intervals match the period of the modules’ resonance frequency. Hence, the mechanism was termed communication through resonance (CTR). This mechanism has three severe constraints, though. First, it needs periodic trains of PPs, whereas single PPs fail to propagate. Second, the inter-PP interval needs to match the network resonance. Third, transmission is very slow, because in each module, the network resonance needs to build up over multiple oscillation cycles. Here, we show that, by adding appropriate feedback connections to the network, the CTR mechanism can be improved and the aforementioned constraints relaxed. Specifically, we show that adding feedback connections between two upstream modules, called the resonance pair, in an otherwise feedforward modular network can support successful propagation of a single PP throughout the entire network. The key condition for successful transmission is that the sum of the forward and backward delays in the resonance pair matches the resonance frequency of the network modules. The transmission is much faster, by more than a factor of two, than in the original CTR mechanism. Moreover, it distinctly lowers the threshold for successful communication by synchronous spiking in modular networks of weakly coupled networks. Thus, our results suggest a new functional role of bidirectional connectivity for the communication in cortical area networks. The cortex is organized as a modular system, with the modules (cortical areas) communicating via weak long-range connections. It has been suggested that the intrinsic resonance properties of population activities in these areas might contribute to enabling successful communication. A module’s intrinsic resonance appears in the damped oscillatory response to an incoming spike volley, enabling successful communication during the peaks of the oscillation. Such communication can be exploited in feedforward networks, provided the participating networks have similar resonance frequencies. This, however, is not necessarily true for cortical networks. Moreover, the communication is slow, as it takes several oscillation cycles to build up the response in the downstream network. Also, only periodic trains of spikes volleys (and not single volleys) with matching intervals can propagate. Here, we present a novel mechanism that alleviates these shortcomings and enables propagation of synchronous spiking across weakly connected networks with not necessarily identical resonance frequencies. In this framework, an individual spike volley can propagate by local amplification through reverberation in a loop between two successive networks, connected by feedforward and feedback connections: the resonance pair. This overcomes the need for activity build-up in downstream networks, causing the volley to propagate distinctly faster and more reliably.
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Affiliation(s)
- Hedyeh Rezaei
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Ad Aertsen
- Faculty of Biology, and Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
| | - Arvind Kumar
- Faculty of Biology, and Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
- Dept. of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
- * E-mail: (AK); (AV)
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Niavaran, Tehran, Iran
- * E-mail: (AK); (AV)
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21
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Hasanzadeh N, Rezaei M, Faraz S, Popovic MR, Lankarany M. Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate. Front Comput Neurosci 2020; 14:64. [PMID: 32848685 PMCID: PMC7405925 DOI: 10.3389/fncom.2020.00064] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 05/28/2020] [Indexed: 11/13/2022] Open
Abstract
Reliable propagation of slow-modulations of the firing rate across multiple layers of a feedforward network (FFN) has proven difficult to capture in spiking neural models. In this paper, we explore necessary conditions for reliable and stable propagation of time-varying asynchronous spikes whose instantaneous rate of changes-in fairly short time windows [20-100] msec-represents information of slow fluctuations of the stimulus. Specifically, we study the effect of network size, level of background synaptic noise, and the variability of synaptic delays in an FFN with all-to-all connectivity. We show that network size and the level of background synaptic noise, together with the strength of synapses, are substantial factors enabling the propagation of asynchronous spikes in deep layers of an FFN. In contrast, the variability of synaptic delays has a minor effect on signal propagation.
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Affiliation(s)
- Navid Hasanzadeh
- Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada.,School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mohammadreza Rezaei
- Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada.,KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Sayan Faraz
- Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Milos R Popovic
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Milad Lankarany
- Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada.,KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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22
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Gaucher Q, Yger P, Edeline JM. Increasing excitation versus decreasing inhibition in auditory cortex: consequences on the discrimination performance between communication sounds. J Physiol 2020; 598:3765-3785. [PMID: 32538485 DOI: 10.1113/jp279902] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/08/2020] [Indexed: 12/18/2022] Open
Abstract
KEY POINTS Enhancing cortical excitability can be achieved by either reducing intracortical inhibition or by enhancing intracortical excitation. Here we compare the consequences of reducing intracortical inhibition and of enhancing intracortical excitation on the processing of communication sounds in the primary auditory cortex. Local application of gabazine and of AMPA enlarged the spectrotemporal receptive fields and increased the responses to communication to the same extent. The Mutual Information (an index of the cortical neurons' ability to discriminate between natural sounds) was increased in both cases, as were the noise and signal correlations. Spike-timing reliability was only increased after gabazine application and post-excitation suppression was affected in the opposite way: it was increased when reducing the intracortical inhibition but was eliminated by enhancing the excitation. A computational model suggests that these results can be explained by an additive effect vs. a multiplicative effect ABSTRACT: The level of excitability of cortical circuits is often viewed as one of the critical factors controlling perceptive performance. In theory, enhancing cortical excitability can be achieved either by reducing inhibitory currents or by increasing excitatory currents. Here, we evaluated whether reducing inhibitory currents or increasing excitatory currents in auditory cortex similarly affects the neurons' ability to discriminate between communication sounds. We attenuated the inhibitory currents by application of gabazine (GBZ), and increased the excitatory currents by applying AMPA in the auditory cortex while testing frequency receptive fields and responses to communication sounds. GBZ and AMPA enlarged the receptive fields and increased the responses to communication sounds to the same extent. The spike-timing reliability of neuronal responses was largely increased when attenuating the intracortical inhibition but not after increasing the excitation. The discriminative abilities of cortical cells increased in both cases but this increase was more pronounced after attenuating the inhibition. The shape of the response to communication sounds was modified in the opposite direction: reducing inhibition increased post-excitation suppression whereas this suppression tended to disappear when increasing the excitation. A computational model indicates that the additive effect promoted by AMPA vs. the multiplicative effect of GBZ on neuronal responses, together with the dynamics of spontaneous cortical activity, can explain these differences. Thus, although apparently equivalent for increasing cortical excitability, acting on inhibition vs. on excitation impacts differently the cortical ability to discriminate natural stimuli, and only modulating inhibition changed efficiently the cortical representation of communication sounds.
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Affiliation(s)
- Quentin Gaucher
- Paris-Saclay Institute of Neurosciences (Neuro-PSI), Department Cognition and Behaviour, CNRS UMR 9197, Orsay Cedex, 91405, France.,Université Paris-Sud, Bâtiment 446, Orsay Cedex, 91405, France
| | - Pierre Yger
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80, CNRS UMR 7210, Paris, France
| | - Jean-Marc Edeline
- Paris-Saclay Institute of Neurosciences (Neuro-PSI), Department Cognition and Behaviour, CNRS UMR 9197, Orsay Cedex, 91405, France.,Université Paris-Sud, Bâtiment 446, Orsay Cedex, 91405, France
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23
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Neural Coding of Thermal Preferences in the Nematode Caenorhabditis elegans. eNeuro 2020; 7:ENEURO.0414-19.2020. [PMID: 32253198 PMCID: PMC7322292 DOI: 10.1523/eneuro.0414-19.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/09/2020] [Accepted: 02/08/2020] [Indexed: 02/02/2023] Open
Abstract
Animals are capable to modify sensory preferences according to past experiences. Surrounded by ever-changing environments, they continue assigning a hedonic value to a sensory stimulus. It remains to be elucidated however how such alteration of sensory preference is encoded in the nervous system. Here we show that past experiences alter temporal interaction between the calcium responses of sensory neurons and their postsynaptic interneurons in the nematode Caenorhabditis elegans. C. elegans exhibits thermotaxis, in which its temperature preference is modified by the past feeding experience: well-fed animals are attracted toward their past cultivation temperature on a thermal gradient, whereas starved animals lose that attraction. By monitoring calcium responses simultaneously from both AFD thermosensory neurons and their postsynaptic AIY interneurons in well-fed and starved animals under time-varying thermal stimuli, we found that past feeding experiences alter phase shift between AFD and AIY calcium responses. Furthermore, the difference in neuronal activities between well-fed and starved animals observed here are able to explain the difference in the behavioral output on a thermal gradient between well-fed and starved animals. Although previous studies have shown that C. elegans executes thermotaxis by regulating amplitude or frequency of the AIY response, our results proposed a new mechanism by which thermal preference is encoded by phase shift between AFD and AIY activities. Given these observations, thermal preference is likely to be computed on synapses between AFD and AIY neurons. Such a neural strategy may enable animals to enrich information processing within defined connectivity via dynamic alterations of synaptic communication.
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24
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Gwak J, Kwag J. Distinct subtypes of inhibitory interneurons differentially promote the propagation of rate and temporal codes in the feedforward neural network. CHAOS (WOODBURY, N.Y.) 2020; 30:053102. [PMID: 32491918 DOI: 10.1063/1.5134765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 04/09/2020] [Indexed: 06/11/2023]
Abstract
Sensory information is believed to be encoded in neuronal spikes using two different neural codes, the rate code (spike firing rate) and the temporal code (precisely-timed spikes). Since the sensory cortex has a highly hierarchical feedforward structure, sensory information-carrying neural codes should reliably propagate across the feedforward network (FFN) of the cortex. Experimental evidence suggests that inhibitory interneurons, such as the parvalbumin-positive (PV) and somatostatin-positive (SST) interneurons, that have distinctively different electrophysiological and synaptic properties, modulate the neural codes during sensory information processing in the cortex. However, how PV and SST interneurons impact on the neural code propagation in the cortical FFN is unknown. We address this question by building a five-layer FFN model consisting of a physiologically realistic Hodgkin-Huxley-type models of excitatory neurons and PV/SST interneurons at different ratios. In response to different firing rate inputs (20-80 Hz), a higher ratio of PV over SST interneurons promoted a reliable propagation of all ranges of firing rate inputs. In contrast, in response to a range of precisely-timed spikes in the form of pulse-packets [with a different number of spikes (α, 40-400 spikes) and degree of dispersion (σ, 0-20 ms)], a higher ratio of SST over PV interneurons promoted a reliable propagation of pulse-packets. Our simulation results show that PV and SST interneurons differentially promote a reliable propagation of the rate and temporal codes, respectively, indicating that the dynamic recruitment of PV and SST interneurons may play critical roles in a reliable propagation of sensory information-carrying neural codes in the cortical FFN.
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Affiliation(s)
- Jeongheon Gwak
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea
| | - Jeehyun Kwag
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea
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25
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Induction and propagation of transient synchronous activity in neural networks endowed with short-term plasticity. Cogn Neurodyn 2020; 15:53-64. [PMID: 33786079 DOI: 10.1007/s11571-020-09578-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 02/25/2020] [Accepted: 03/06/2020] [Indexed: 12/11/2022] Open
Abstract
Transient, task related synchronous activity within neural populations has been recognized as the substrate of temporal coding in the brain. The mechanisms underlying inducing and propagation of transient synchronous activity are still unknown, and we propose that short-term plasticity (STP) of neural circuits may serve as a supplemental mechanism therein. By computational modeling, we showed that short-term facilitation greatly increases the reactivation rate of population spikes and decreases the latency of response to reactivation stimuli in local recurrent neural networks. Meanwhile, the timing of population spike reactivation is controlled by the memory effect of STP, and it is mediated primarily by the facilitation time constant. Furthermore, we demonstrated that synaptic facilitation dramatically enhances synchrony propagation in feedforward neural networks and that response timing mediated by synaptic facilitation offers a scheme for information routing. In addition, we verified that synaptic strengthening of intralayer or interlayer coupling enhances synchrony propagation, and we verified that other factors such as the delay of synaptic transmission and the mode of synaptic connectivity are also involved in regulating synchronous activity propagation. Overall, our results highlight the functional role of STP in regulating the inducing and propagation of transient synchronous activity, and they may inspire testable hypotheses for future experimental studies.
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26
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Wu J, Aton SJ, Booth V, Zochowski M. Network and cellular mechanisms underlying heterogeneous excitatory/inhibitory balanced states. Eur J Neurosci 2020; 51:1624-1641. [PMID: 31903627 DOI: 10.1111/ejn.14669] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 12/27/2019] [Accepted: 01/02/2020] [Indexed: 11/28/2022]
Abstract
Recent work has explored spatiotemporal relationships between excitatory (E) and inhibitory (I) signaling within neural networks, and the effect of these relationships on network activity patterns. Data from these studies have indicated that excitation and inhibition are maintained at a similar level across long time periods and that excitatory and inhibitory currents may be tightly synchronized. Disruption of this balance-leading to an aberrant E/I ratio-is implicated in various brain pathologies. However, a thorough characterization of the relationship between E and I currents in experimental settings is largely impossible, due to their tight regulation at multiple cellular and network levels. Here, we use biophysical neural network models to investigate the emergence and properties of balanced states by heterogeneous mechanisms. Our results show that a network can homeostatically regulate the E/I ratio through interactions among multiple cellular and network factors, including average firing rates, synaptic weights and average neural depolarization levels in excitatory/inhibitory populations. Complex and competing interactions between firing rates and depolarization levels allow these factors to alternately dominate network dynamics in different synaptic weight regimes. This leads to the emergence of distinct mechanisms responsible for determining a balanced state and its dynamical correlate. Our analysis provides a comprehensive picture of how E/I ratio changes when manipulating specific network properties, and identifies the mechanisms regulating E/I balance. These results provide a framework to explain the diverse, and in some cases, contradictory experimental observations on the E/I state in different brain states and conditions.
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Affiliation(s)
- Jiaxing Wu
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA
| | - Sara J Aton
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Victoria Booth
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA.,Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Michal Zochowski
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.,Department of Physics, University of Michigan, Ann Arbor, MI, USA.,Biophysics Program, University of Michigan, Ann Arbor, MI, USA
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27
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Abstract
The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.
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28
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Zajzon B, Mahmoudian S, Morrison A, Duarte R. Passing the Message: Representation Transfer in Modular Balanced Networks. Front Comput Neurosci 2019; 13:79. [PMID: 31920605 PMCID: PMC6915101 DOI: 10.3389/fncom.2019.00079] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 10/29/2019] [Indexed: 01/08/2023] Open
Abstract
Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems' computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust, and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge toward which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks.
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Affiliation(s)
- Barna Zajzon
- Jülich Research Centre, Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1/INM-10), Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Sepehr Mahmoudian
- Department of Data-Driven Analysis of Biological Networks, Campus Institute for Dynamics of Biological Networks, Georg August University Göttingen, Göttingen, Germany
- MEG Unit, Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Abigail Morrison
- Jülich Research Centre, Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1/INM-10), Jülich, Germany
- Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany
| | - Renato Duarte
- Jülich Research Centre, Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1/INM-10), Jülich, Germany
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29
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Yuan M, Wu X, Yan R, Tang H. Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses. Neural Comput 2019; 31:2368-2389. [PMID: 31614099 DOI: 10.1162/neco_a_01238] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking neurons involve only a single plasticity mechanism. Here, we propose a neural realistic reinforcement learning model that coordinates the plasticities of two types of synapses: stochastic and deterministic. The plasticity of the stochastic synapse is achieved by the hedonistic rule through modulating the release probability of synaptic neurotransmitter, while the plasticity of the deterministic synapse is achieved by a variant of a reward-modulated spike-timing-dependent plasticity rule through modulating the synaptic strengths. We evaluate the proposed learning model on two benchmark tasks: learning a logic gate function and the 19-state random walk problem. Experimental results show that the coordination of diverse synaptic plasticities can make the RL model learn in a rapid and stable form.
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Affiliation(s)
- Mengwen Yuan
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Xi Wu
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Rui Yan
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Huajin Tang
- College of Computer Science, Sichuan University, Chengdu 610065, China, and College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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Barral J, Wang XJ, Reyes AD. Propagation of temporal and rate signals in cultured multilayer networks. Nat Commun 2019; 10:3969. [PMID: 31481671 PMCID: PMC6722076 DOI: 10.1038/s41467-019-11851-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 07/31/2019] [Indexed: 11/08/2022] Open
Abstract
Analyses of idealized feedforward networks suggest that several conditions have to be satisfied in order for activity to propagate faithfully across layers. Verifying these concepts experimentally has been difficult owing to the vast number of variables that must be controlled. Here, we cultured cortical neurons in a chamber with sequentially connected compartments, optogenetically stimulated individual neurons in the first layer with high spatiotemporal resolution, and then monitored the subthreshold and suprathreshold potentials in subsequent layers. Brief stimuli delivered to the first layer evoked a short-latency transient response followed by sustained activity. Rate signals, carried by the sustained component, propagated reliably through 4 layers, unlike idealized feedforward networks, which tended strongly towards synchrony. Moreover, temporal jitter in the stimulus was transformed into a rate code and transmitted to the last layer. This novel mode of propagation occurred in the balanced excitatory-inhibitory regime and is mediated by NMDA-mediated receptors and recurrent activity.
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Affiliation(s)
- Jérémie Barral
- Center for Neural Science, New York University, New York, NY, USA.
- Institut de l'Audition, Institut Pasteur, Paris, France.
| | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA
| | - Alex D Reyes
- Center for Neural Science, New York University, New York, NY, USA
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31
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Bhatia A, Moza S, Bhalla US. Precise excitation-inhibition balance controls gain and timing in the hippocampus. eLife 2019; 8:43415. [PMID: 31021319 PMCID: PMC6517031 DOI: 10.7554/elife.43415] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 04/10/2019] [Indexed: 12/19/2022] Open
Abstract
Excitation-inhibition (EI) balance controls excitability, dynamic range, and input gating in many brain circuits. Subsets of synaptic input can be selected or 'gated' by precise modulation of finely tuned EI balance, but assessing the granularity of EI balance requires combinatorial analysis of excitatory and inhibitory inputs. Using patterned optogenetic stimulation of mouse hippocampal CA3 neurons, we show that hundreds of unique CA3 input combinations recruit excitation and inhibition with a nearly identical ratio, demonstrating precise EI balance at the hippocampus. Crucially, the delay between excitation and inhibition decreases as excitatory input increases from a few synapses to tens of synapses. This creates a dynamic millisecond-range window for postsynaptic excitation, controlling membrane depolarization amplitude and timing via subthreshold divisive normalization. We suggest that this combination of precise EI balance and dynamic EI delays forms a general mechanism for millisecond-range input gating and subthreshold gain control in feedforward networks.
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Affiliation(s)
- Aanchal Bhatia
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Sahil Moza
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Upinder Singh Bhalla
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
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Duarte R, Morrison A. Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. PLoS Comput Biol 2019; 15:e1006781. [PMID: 31022182 PMCID: PMC6504118 DOI: 10.1371/journal.pcbi.1006781] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 05/07/2019] [Accepted: 01/09/2019] [Indexed: 11/24/2022] Open
Abstract
Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every process, at every scale, and are inextricably linked to the systems' emergent collective behaviours and function. However, the majority of studies addressing the dynamics and computational properties of biologically inspired cortical microcircuits tend to assume (often for the sake of analytical tractability) a great degree of homogeneity in both neuronal and synaptic/connectivity parameters. While simplification and reductionism are necessary to understand the brain's functional principles, disregarding the existence of the multiple heterogeneities in the cortical composition, which may be at the core of its computational proficiency, will inevitably fail to account for important phenomena and limit the scope and generalizability of cortical models. We address these issues by studying the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data. This approach was made possible by the emergence of large-scale, well curated databases, as well as the substantial improvements in experimental methodologies achieved over the last few years. Our results show that variability in single neuron parameters is the dominant source of functional specialization, leading to highly proficient microcircuits with much higher computational power than their homogeneous counterparts. We further show that fully heterogeneous circuits, which are closest to the biophysical reality, owe their response properties to the differential contribution of different sources of heterogeneity.
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Affiliation(s)
- Renato Duarte
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Jülich Research Centre, Jülich, Germany
- Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, Germany
- Faculty of Biology, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, Germany
- Institute of Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Jülich Research Centre, Jülich, Germany
- Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, Germany
- Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, Bochum, Germany
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Short-Term Plasticity Combines with Excitation-Inhibition Balance to Expand Cerebellar Purkinje Cell Dynamic Range. J Neurosci 2018; 38:5153-5167. [PMID: 29720550 DOI: 10.1523/jneurosci.3270-17.2018] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 04/11/2018] [Accepted: 04/21/2018] [Indexed: 01/03/2023] Open
Abstract
The balance between excitation (E) and inhibition (I) in neuronal networks controls the firing rate of principal cells through simple network organization, such as feedforward inhibitory circuits. Here, we demonstrate in male mice, that at the granule cell (GrC)-molecular layer interneuron (MLI)-Purkinje cell (PC) pathway of the cerebellar cortex, E/I balance is dynamically controlled by short-term dynamics during bursts of stimuli, shaping cerebellar output. Using a combination of electrophysiological recordings, optogenetic stimulation, and modeling, we describe the wide range of bidirectional changes in PC discharge triggered by GrC bursts, from robust excitation to complete inhibition. At high frequency (200 Hz), increasing the number of pulses in a burst (from 3 to 7) can switch a net inhibition of PC to a net excitation. Measurements of EPSCs and IPSCs during bursts and modeling showed that this feature can be explained by the interplay between short-term dynamics of the GrC-MLI-PC pathway and E/I balance impinging on PC. Our findings demonstrate that PC firing rate is highly sensitive to the duration of GrC bursts, which may define a temporal-to-rate code transformation in the cerebellar cortex.SIGNIFICANCE STATEMENT Sensorimotor information processing in the cerebellar cortex leads to the occurrence of a sequence of synaptic excitation and inhibition in Purkinje cells. Granule cells convey direct excitatory inputs and indirect inhibitory inputs to the Purkinje cells, through molecular layer interneurons, forming a feedforward inhibitory pathway. Using electrophysiological recordings, optogenetic stimulation, and mathematical modeling, we found that presynaptic short-term dynamics affect the balance between synaptic excitation and inhibition on Purkinje cells during high-frequency bursts and can reverse the sign of granule cell influence on Purkinje cell discharge when burst duration increases. We conclude that short-term dynamics may play an important role in transforming the duration of sensory inputs arriving on cerebellar granule cells into cerebellar cortical output firing rate.
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Joglekar MR, Mejias JF, Yang GR, Wang XJ. Inter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex. Neuron 2018; 98:222-234.e8. [DOI: 10.1016/j.neuron.2018.02.031] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 12/27/2017] [Accepted: 02/27/2018] [Indexed: 01/19/2023]
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Hennequin G, Agnes EJ, Vogels TP. Inhibitory Plasticity: Balance, Control, and Codependence. Annu Rev Neurosci 2017; 40:557-579. [DOI: 10.1146/annurev-neuro-072116-031005] [Citation(s) in RCA: 140] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Guillaume Hennequin
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
| | - Everton J. Agnes
- Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3SR, United Kingdom
| | - Tim P. Vogels
- Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3SR, United Kingdom
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Sprekeler H. Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond. Curr Opin Neurobiol 2017; 43:198-203. [PMID: 28500933 DOI: 10.1016/j.conb.2017.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Revised: 03/12/2017] [Accepted: 03/22/2017] [Indexed: 11/18/2022]
Abstract
Computational neuroscience has a long-standing tradition of investigating the consequences of excitatory synaptic plasticity. In contrast, the functions of inhibitory plasticity are still largely nebulous, particularly given the bewildering diversity of interneurons in the brain. Here, we review recent computational advances that provide first suggestions for the functional roles of inhibitory plasticity, such as a maintenance of the excitation-inhibition balance, a stabilization of recurrent network dynamics and a decorrelation of sensory responses. The field is still in its infancy, but given the existing body of theory for excitatory plasticity, it is likely to mature quickly and deliver important insights into the self-organization of inhibitory circuits in the brain.
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Affiliation(s)
- Henning Sprekeler
- Department for Electrical Engineering and Computer Science, Berlin Institute of Technology, and Bernstein Center for Computational Neuroscience, Marchstr. 23, 10587 Berlin, Germany.
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Belić JJ, Kumar A, Hellgren Kotaleski J. Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations. PLoS One 2017; 12:e0175135. [PMID: 28384268 PMCID: PMC5383243 DOI: 10.1371/journal.pone.0175135] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 03/21/2017] [Indexed: 11/19/2022] Open
Abstract
Network oscillations are ubiquitous across many brain regions. In the basal ganglia, oscillations are also present at many levels and a wide range of characteristic frequencies have been reported to occur during both health and disease. The striatum, the main input nucleus of the basal ganglia, receives massive glutamatergic inputs from the cortex and is highly susceptible to external oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, external stimuli propagate through the striatal circuitry. Using a network model of the striatum and corticostriatal projections, we try to elucidate the importance of specific GABAergic neurons and their interactions in shaping striatal oscillatory activity. Here, we propose that fast-spiking interneurons can perform an important role in transferring cortical oscillations to the striatum especially to those medium spiny neurons that are not directly driven by the cortical oscillations. We show how the activity levels of different populations, the strengths of different inhibitory synapses, degree of outgoing projections of striatal cells, ongoing activity and synchronicity of inputs can influence network activity. These results suggest that the propagation of oscillatory inputs into the medium spiny neuron population is most efficient, if conveyed via the fast-spiking interneurons. Therefore, pharmaceuticals that target fast-spiking interneurons may provide a novel treatment for regaining the spectral characteristics of striatal activity that correspond to the healthy state.
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Affiliation(s)
- Jovana J. Belić
- Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
- Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
| | - Arvind Kumar
- Department of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
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Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition. Nat Commun 2017; 8:14566. [PMID: 28281523 PMCID: PMC5353699 DOI: 10.1038/ncomms14566] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2016] [Accepted: 01/09/2017] [Indexed: 11/23/2022] Open
Abstract
Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory, we show that optimal IPSG parameters can be learned through anti-Hebbian rules. Finally, we compare our theoretical optima to published experimental data from 21 types of neurons, in which rates of synaptic excitation and IPSG decay times vary by factors of about 100 (5–600 Hz) and 50 (1–50 ms), respectively. From an infinite range of possible decay times, theory predicted experimental decay times within less than a factor of 2. Across a distinct set of 15 types of neuron recorded in vivo, theory predicted the amplitude of synaptic inhibition within a factor of 1.7. Thus, the theory can explain biophysical quantities from first principles. Inhibition and excitation are counterbalanced at synapses, but the conditions that constitute optimal balance are not known. Here the authors show through modelling that the properties of synaptic inhibition are fine-tuned to maintain an optimal balance in which peak excitation reaches precisely to spike threshold.
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Yang DP, Zhou HJ, Zhou C. Co-emergence of multi-scale cortical activities of irregular firing, oscillations and avalanches achieves cost-efficient information capacity. PLoS Comput Biol 2017; 13:e1005384. [PMID: 28192429 PMCID: PMC5330539 DOI: 10.1371/journal.pcbi.1005384] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Revised: 02/28/2017] [Accepted: 01/29/2017] [Indexed: 11/19/2022] Open
Abstract
The brain is highly energy consuming, therefore is under strong selective pressure to achieve cost-efficiency in both cortical connectivities and activities. However, cost-efficiency as a design principle for cortical activities has been rarely studied. Especially it is not clear how cost-efficiency is related to ubiquitously observed multi-scale properties: irregular firing, oscillations and neuronal avalanches. Here we demonstrate that these prominent properties can be simultaneously observed in a generic, biologically plausible neural circuit model that captures excitation-inhibition balance and realistic dynamics of synaptic conductance. Their co-emergence achieves minimal energy cost as well as maximal energy efficiency on information capacity, when neuronal firing are coordinated and shaped by moderate synchrony to reduce otherwise redundant spikes, and the dynamical clusterings are maintained in the form of neuronal avalanches. Such cost-efficient neural dynamics can be employed as a foundation for further efficient information processing under energy constraint. The adult human brain consumes more than 20% of the resting metabolism, despite constituting only 2% of the body’s mass. Most energy is consumed by the cerebral cortex with billions of neurons, mainly to restore ion gradients across membranes for generating and propagating action potentials and synaptic transmission. Even small increases in the average spike rate of cortical neurons could cause the cortex to exceed the energy budget for the whole brain. Consequently, the cortex is likely to be under considerable selective pressure to reduce spike rates but to maintain efficient information processing. Experimentally, cortical activities are ubiquitously observed at multiple scales with prominent features: irregular individual firing, synchronized oscillations and neuronal avalanches. Do these features of cortical activities reflect cost-efficiency on the aspect of information capacity? We employ a generic but biologically plausible local neural circuit to compare various dynamical modes with different degrees of synchrony. Our simulations show that these features of cortical activities can be observed simultaneously and their co-emergence indeed robustly achieves maximal energy efficiency and minimal energy cost. Our work thus suggests that basic neurobiological and dynamical mechanisms can support the foundation for efficient neural information processing under the energy constraint.
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Affiliation(s)
- Dong-Ping Yang
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- * E-mail: (DPY); (CZ)
| | - Hai-Jun Zhou
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China
| | - Changsong Zhou
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Beijing Computational Science Research Center, Beijing, China
- Research Center, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China
- * E-mail: (DPY); (CZ)
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Han R, Wang J, Miao R, Deng B, Qin Y, Yu H, Wei X. Propagation of Collective Temporal Regularity in Noisy Hierarchical Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017; 28:191-205. [PMID: 28055909 DOI: 10.1109/tnnls.2015.2502993] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Neuronal communication between different brain areas is achieved in terms of spikes. Consequently, spike-time regularity is closely related to many cognitive tasks and timing precision of neural information processing. A recent experiment on primate parietal cortex reports that spike-time regularity increases consistently from primary sensory to higher cortical regions. This observation conflicts with the influential view that spikes in the neocortex are fundamentally irregular. To uncover the underlying network mechanism, we construct a multilayered feedforward neural information transmission pathway and investigate how spike-time regularity evolves across subsequent layers. Numerical results reveal that despite the obviously irregular spiking patterns in previous several layers, neurons in downstream layers can generate rather regular spikes, which depends on the network topology. In particular, we find that collective temporal regularity in deeper layers exhibits resonance-like behavior with respect to both synaptic connection probability and synaptic weight, i.e., the optimal topology parameter maximizes the spike-timing regularity. Furthermore, it is demonstrated that synaptic properties, including inhibition, synaptic transient dynamics, and plasticity, have significant impacts on spike-timing regularity propagation. The emergence of the increasingly regular spiking (RS) patterns in higher parietal regions can, thus, be viewed as a natural consequence of spiking activity propagation between different brain areas. Finally, we validate an important function served by increased RS: promoting reliable propagation of spike-rate signals across downstream layers.
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Dipoppa M, Szwed M, Gutkin BS. Controlling Working Memory Operations by Selective Gating: The Roles of Oscillations and Synchrony. Adv Cogn Psychol 2016; 12:209-232. [PMID: 28154616 PMCID: PMC5280056 DOI: 10.5709/acp-0199-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 10/18/2016] [Indexed: 11/23/2022] Open
Abstract
Working memory (WM) is a primary cognitive function that corresponds to the ability to update, stably maintain, and manipulate short-term memory (ST M) rapidly to perform ongoing cognitive tasks. A prevalent neural substrate of WM coding is persistent neural activity, the property of neurons to remain active after having been activated by a transient sensory stimulus. This persistent activity allows for online maintenance of memory as well as its active manipulation necessary for task performance. WM is tightly capacity limited. Therefore, selective gating of sensory and internally generated information is crucial for WM function. While the exact neural substrate of selective gating remains unclear, increasing evidence suggests that it might be controlled by modulating ongoing oscillatory brain activity. Here, we review experiments and models that linked selective gating, persistent activity, and brain oscillations, putting them in the more general mechanistic context of WM. We do so by defining several operations necessary for successful WM function and then discussing how such operations may be carried out by mechanisms suggested by computational models. We specifically show how oscillatory mechanisms may provide a rapid and flexible active gating mechanism for WM operations.
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Affiliation(s)
- Mario Dipoppa
- Institute of Neurology, Faculty of Brain Sciences, University College
London, UK
| | - Marcin Szwed
- Departement of Psychology, Jagiellonian University, Kraków,
Poland
| | - Boris S. Gutkin
- Center for Cognition and Decision Making, NR U HSE , Moscow,
Russia
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Kremkow J, Perrinet LU, Monier C, Alonso JM, Aertsen A, Frégnac Y, Masson GS. Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1. Front Neural Circuits 2016; 10:37. [PMID: 27242445 PMCID: PMC4862982 DOI: 10.3389/fncir.2016.00037] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 04/25/2016] [Indexed: 11/13/2022] Open
Abstract
Neurons in the primary visual cortex are known for responding vigorously but with high variability to classical stimuli such as drifting bars or gratings. By contrast, natural scenes are encoded more efficiently by sparse and temporal precise spiking responses. We used a conductance-based model of the visual system in higher mammals to investigate how two specific features of the thalamo-cortical pathway, namely push-pull receptive field organization and fast synaptic depression, can contribute to this contextual reshaping of V1 responses. By comparing cortical dynamics evoked respectively by natural vs. artificial stimuli in a comprehensive parametric space analysis, we demonstrate that the reliability and sparseness of the spiking responses during natural vision is not a mere consequence of the increased bandwidth in the sensory input spectrum. Rather, it results from the combined impacts of fast synaptic depression and push-pull inhibition, the later acting for natural scenes as a form of “effective” feed-forward inhibition as demonstrated in other sensory systems. Thus, the combination of feedforward-like inhibition with fast thalamo-cortical synaptic depression by simple cells receiving a direct structured input from thalamus composes a generic computational mechanism for generating a sparse and reliable encoding of natural sensory events.
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Affiliation(s)
- Jens Kremkow
- Institut de Neurosciences de la Timone, UMR 7289, Centre National de la Recherche Scientifique - Aix-Marseille UniversitéMarseille, France; Neurobiology and Biophysics, Faculty of Biology, University of FreiburgFreiburg, Germany; Bernstein Center Freiburg, University of FreiburgFreiburg, Germany; Department of Biological Sciences, State University of New York (SUNY-Optometry)New York, NY, USA
| | - Laurent U Perrinet
- Institut de Neurosciences de la Timone, UMR 7289, Centre National de la Recherche Scientifique - Aix-Marseille Université Marseille, France
| | - Cyril Monier
- Unité de Neurosciences, Information et Complexité, UPR Centre National de la Recherche Scientifique 3293 Gif-sur-Yvette, France
| | - Jose-Manuel Alonso
- Department of Biological Sciences, State University of New York (SUNY-Optometry) New York, NY, USA
| | - Ad Aertsen
- Neurobiology and Biophysics, Faculty of Biology, University of FreiburgFreiburg, Germany; Bernstein Center Freiburg, University of FreiburgFreiburg, Germany
| | - Yves Frégnac
- Unité de Neurosciences, Information et Complexité, UPR Centre National de la Recherche Scientifique 3293 Gif-sur-Yvette, France
| | - Guillaume S Masson
- Institut de Neurosciences de la Timone, UMR 7289, Centre National de la Recherche Scientifique - Aix-Marseille Université Marseille, France
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Chan HK, Yang DP, Zhou C, Nowotny T. Burst Firing Enhances Neural Output Correlation. Front Comput Neurosci 2016; 10:42. [PMID: 27242499 PMCID: PMC4860405 DOI: 10.3389/fncom.2016.00042] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 04/18/2016] [Indexed: 11/13/2022] Open
Abstract
Neurons communicate and transmit information predominantly through spikes. Given that experimentally observed neural spike trains in a variety of brain areas can be highly correlated, it is important to investigate how neurons process correlated inputs. Most previous work in this area studied the problem of correlation transfer analytically by making significant simplifications on neural dynamics. Temporal correlation between inputs that arises from synaptic filtering, for instance, is often ignored when assuming that an input spike can at most generate one output spike. Through numerical simulations of a pair of leaky integrate-and-fire (LIF) neurons receiving correlated inputs, we demonstrate that neurons in the presence of synaptic filtering by slow synapses exhibit strong output correlations. We then show that burst firing plays a central role in enhancing output correlations, which can explain the above-mentioned observation because synaptic filtering induces bursting. The observed changes of correlations are mostly on a long time scale. Our results suggest that other features affecting the prevalence of neural burst firing in biological neurons, e.g., adaptive spiking mechanisms, may play an important role in modulating the overall level of correlations in neural networks.
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Affiliation(s)
- Ho Ka Chan
- Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of SussexBrighton, UK
- Department of Physics, Hong Kong Baptist UniversityKowloon Tong, Hong Kong
- Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist UniversityKowloon Tong, Hong Kong
| | - Dong-Ping Yang
- Department of Physics, Hong Kong Baptist UniversityKowloon Tong, Hong Kong
- Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist UniversityKowloon Tong, Hong Kong
- School of Physics, University of SydneyNew South Wales, Sydney, NSW, Australia
| | - Changsong Zhou
- Department of Physics, Hong Kong Baptist UniversityKowloon Tong, Hong Kong
- Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist UniversityKowloon Tong, Hong Kong
| | - Thomas Nowotny
- Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of SussexBrighton, UK
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Shein-Idelson M, Cohen G, Ben-Jacob E, Hanein Y. Modularity Induced Gating and Delays in Neuronal Networks. PLoS Comput Biol 2016; 12:e1004883. [PMID: 27104350 PMCID: PMC4841573 DOI: 10.1371/journal.pcbi.1004883] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 03/24/2016] [Indexed: 11/23/2022] Open
Abstract
Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. The capacity to transmit information between connected parts of a neuronal network is fundamental to its function. The organization of network connections (the topology of the network) is therefore expected to play an important role in determining network transmission. Since modular topology characterizes many brain circuits on multiple scales, investigating the role of modularity in activity gating is clearly desirable. By engineering such modular networks in vitro, we were able to perform such an investigation. Under these experimental conditions, we can independently control the degree of modularity, as well as inhibition in the network. We show that a combination of these two properties is highly beneficial from a communication perspective. Namely, it equips connected modules and large modular networks with the capacity to gate and temporally coordinate activity between the different parts of the network.
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Affiliation(s)
- Mark Shein-Idelson
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
- Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv, Israel
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- * E-mail:
| | - Gilad Cohen
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
- Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv, Israel
| | - Eshel Ben-Jacob
- Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv, Israel
- School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Yael Hanein
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
- Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Letzkus J, Wolff S, Lüthi A. Disinhibition, a Circuit Mechanism for Associative Learning and Memory. Neuron 2015; 88:264-76. [PMID: 26494276 DOI: 10.1016/j.neuron.2015.09.024] [Citation(s) in RCA: 235] [Impact Index Per Article: 26.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Cadherin-13, a risk gene for ADHD and comorbid disorders, impacts GABAergic function in hippocampus and cognition. Transl Psychiatry 2015; 5:e655. [PMID: 26460479 PMCID: PMC4930129 DOI: 10.1038/tp.2015.152] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 07/11/2015] [Indexed: 12/14/2022] Open
Abstract
Cadherin-13 (CDH13), a unique glycosylphosphatidylinositol-anchored member of the cadherin family of cell adhesion molecules, has been identified as a risk gene for attention-deficit/hyperactivity disorder (ADHD) and various comorbid neurodevelopmental and psychiatric conditions, including depression, substance abuse, autism spectrum disorder and violent behavior, while the mechanism whereby CDH13 dysfunction influences pathogenesis of neuropsychiatric disorders remains elusive. Here we explored the potential role of CDH13 in the inhibitory modulation of brain activity by investigating synaptic function of GABAergic interneurons. Cellular and subcellular distribution of CDH13 was analyzed in the murine hippocampus and a mouse model with a targeted inactivation of Cdh13 was generated to evaluate how CDH13 modulates synaptic activity of hippocampal interneurons and behavioral domains related to psychopathologic (endo)phenotypes. We show that CDH13 expression in the cornu ammonis (CA) region of the hippocampus is confined to distinct classes of interneurons. Specifically, CDH13 is expressed by numerous parvalbumin and somatostatin-expressing interneurons located in the stratum oriens, where it localizes to both the soma and the presynaptic compartment. Cdh13(-/-) mice show an increase in basal inhibitory, but not excitatory, synaptic transmission in CA1 pyramidal neurons. Associated with these alterations in hippocampal function, Cdh13(-/-) mice display deficits in learning and memory. Taken together, our results indicate that CDH13 is a negative regulator of inhibitory synapses in the hippocampus, and provide insights into how CDH13 dysfunction may contribute to the excitatory/inhibitory imbalance observed in neurodevelopmental disorders, such as ADHD and autism.
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Naud R, Houtman D, Rose GJ, Longtin A. Counting on dis-inhibition: a circuit motif for interval counting and selectivity in the anuran auditory system. J Neurophysiol 2015; 114:2804-15. [PMID: 26334004 DOI: 10.1152/jn.00138.2015] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 09/01/2015] [Indexed: 02/06/2023] Open
Abstract
Information can be encoded in the temporal patterning of spikes. How the brain reads these patterns is of general importance and represents one of the greatest challenges in neuroscience. We addressed this issue in relation to temporal pattern recognition in the anuran auditory system. Many species of anurans perform mating decisions based on the temporal structure of advertisement calls. One important temporal feature is the number of sound pulses that occur with a species-specific interpulse interval. Neurons representing this pulse count have been recorded in the anuran inferior colliculus, but the mechanisms underlying their temporal selectivity are incompletely understood. Here, we construct a parsimonious model that can explain the key dynamical features of these cells with biologically plausible elements. We demonstrate that interval counting arises naturally when combining interval-selective inhibition with pulse-per-pulse excitation having both fast- and slow-conductance synapses. Interval-dependent inhibition is modeled here by a simple architecture based on known physiology of afferent nuclei. Finally, we consider simple implementations of previously proposed mechanistic explanations for these counting neurons and show that they do not account for all experimental observations. Our results demonstrate that tens of millisecond-range temporal selectivities can arise from simple connectivity motifs of inhibitory neurons, without recourse to internal clocks, spike-frequency adaptation, or appreciable short-term plasticity.
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Affiliation(s)
- Richard Naud
- Department of Physics, University of Ottawa, Ottawa, Canada; and
| | - Dave Houtman
- Department of Physics, University of Ottawa, Ottawa, Canada; and
| | - Gary J Rose
- Department of Biology, University of Utah, Salt Lake City, Utah
| | - André Longtin
- Department of Physics, University of Ottawa, Ottawa, Canada; and
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Abstract
How sensory information is processed within olfactory cortices is unclear. Here, we examined long-range circuit wiring between different olfactory cortical regions of acute mouse brain slices using a channelrhodopsin-2 (ChR2)-based neuronal targeting approach. Our results provide detailed information regarding the synaptic properties of the reciprocal long-range monosynaptic glutamatergic projections (LRMGP) between and within anterior piriform cortex (aPC), posterior piriform cortex (pPC), and lateral entorhinal cortex (LEC), thereby creating a long-range inter- and intracortical circuit diagrams at the level of synapses and single cortical neurons. Our results reveal the following information regarding hierarchical intra- and intercortical organizations: (i) there is massive bottom-up (i.e., rostral-caudal) excitation within the LRMGP accompanied with strong feedforward (FF) inhibition; (ii) there are convergent FF connections onto LEC from both aPC and pPC; (iii) feedback (FB) intercortical connections are weak with a significant fraction of presumptive silent synapses; and (iv) intra and intercortical long-range connections lack layer specificity and their innervation of interneurons are stronger than neighboring pyramidal neurons. The elucidation of the distinct hierarchical organization of long-range olfactory cortical circuits paves the way for further understanding of higher order cortical processing within the olfactory system.
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Affiliation(s)
- Weiguo Yang
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY Graduate Neuroscience Program, University of Wyoming, Laramie, WY
| | - Qian-Quan Sun
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY
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Kudryashova IV. The plasticity of inhibitory synapses as a factor of long-term modifications. NEUROCHEM J+ 2015. [DOI: 10.1134/s1819712415030058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Sornborger AT, Wang Z, Tao L. A mechanism for graded, dynamically routable current propagation in pulse-gated synfire chains and implications for information coding. J Comput Neurosci 2015; 39:181-95. [PMID: 26227067 DOI: 10.1007/s10827-015-0570-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 07/13/2015] [Accepted: 07/15/2015] [Indexed: 10/23/2022]
Abstract
Neural oscillations can enhance feature recognition (Azouz and Gray Proceedings of the National Academy of Sciences of the United States of America, 97, 8110-8115 2000), modulate interactions between neurons (Womelsdorf et al. Science, 316, 1609-01612 2007), and improve learning and memory (Markowska et al. The Journal of Neuroscience, 15, 2063-2073 1995). Numerical studies have shown that coherent spiking can give rise to windows in time during which information transfer can be enhanced in neuronal networks (Abeles Israel Journal of Medical Sciences, 18, 83-92 1982; Lisman and Idiart Science, 267, 1512-1515 1995, Salinas and Sejnowski Nature Reviews. Neuroscience, 2, 539-550 2001). Unanswered questions are: 1) What is the transfer mechanism? And 2) how well can a transfer be executed? Here, we present a pulse-based mechanism by which a graded current amplitude may be exactly propagated from one neuronal population to another. The mechanism relies on the downstream gating of mean synaptic current amplitude from one population of neurons to another via a pulse. Because transfer is pulse-based, information may be dynamically routed through a neural circuit with fixed connectivity. We demonstrate the transfer mechanism in a realistic network of spiking neurons and show that it is robust to noise in the form of pulse timing inaccuracies, random synaptic strengths and finite size effects. We also show that the mechanism is structurally robust in that it may be implemented using biologically realistic pulses. The transfer mechanism may be used as a building block for fast, complex information processing in neural circuits. We show that the mechanism naturally leads to a framework wherein neural information coding and processing can be considered as a product of linear maps under the active control of a pulse generator. Distinct control and processing components combine to form the basis for the binding, propagation, and processing of dynamically routed information within neural pathways. Using our framework, we construct example neural circuits to 1) maintain a short-term memory, 2) compute time-windowed Fourier transforms, and 3) perform spatial rotations. We postulate that such circuits, with automatic and stereotyped control and processing of information, are the neural correlates of Crick and Koch's zombie modes.
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Affiliation(s)
| | - Zhuo Wang
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing, China.
| | - Louis Tao
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, and Center for Quantitative Biology, Peking University, Beijing, China.
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