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Payne HL, Raymond JL, Goldman MS. Interactions between circuit architecture and plasticity in a closed-loop cerebellar system. eLife 2024; 13:e84770. [PMID: 38451856 PMCID: PMC10919899 DOI: 10.7554/elife.84770] [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: 11/08/2022] [Accepted: 02/13/2024] [Indexed: 03/09/2024] Open
Abstract
Determining the sites and directions of plasticity underlying changes in neural activity and behavior is critical for understanding mechanisms of learning. Identifying such plasticity from neural recording data can be challenging due to feedback pathways that impede reasoning about cause and effect. We studied interactions between feedback, neural activity, and plasticity in the context of a closed-loop motor learning task for which there is disagreement about the loci and directions of plasticity: vestibulo-ocular reflex learning. We constructed a set of circuit models that differed in the strength of their recurrent feedback, from no feedback to very strong feedback. Despite these differences, each model successfully fit a large set of neural and behavioral data. However, the patterns of plasticity predicted by the models fundamentally differed, with the direction of plasticity at a key site changing from depression to potentiation as feedback strength increased. Guided by our analysis, we suggest how such models can be experimentally disambiguated. Our results address a long-standing debate regarding cerebellum-dependent motor learning, suggesting a reconciliation in which learning-related changes in the strength of synaptic inputs to Purkinje cells are compatible with seemingly oppositely directed changes in Purkinje cell spiking activity. More broadly, these results demonstrate how changes in neural activity over learning can appear to contradict the sign of the underlying plasticity when either internal feedback or feedback through the environment is present.
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Affiliation(s)
- Hannah L Payne
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | | | - Mark S Goldman
- Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California, DavisDavisUnited States
- Department of Ophthalmology and Vision Science, University of California, DavisDavisUnited States
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2
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Kim CM, Finkelstein A, Chow CC, Svoboda K, Darshan R. Distributing task-related neural activity across a cortical network through task-independent connections. Nat Commun 2023; 14:2851. [PMID: 37202424 DOI: 10.1038/s41467-023-38529-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 05/05/2023] [Indexed: 05/20/2023] Open
Abstract
Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.
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Affiliation(s)
- Christopher M Kim
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Arseny Finkelstein
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Carson C Chow
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Ran Darshan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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3
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Thivierge JP, Giraud E, Lynn M, Théberge Charbonneau A. Key role of neuronal diversity in structured reservoir computing. CHAOS (WOODBURY, N.Y.) 2022; 32:113130. [PMID: 36456321 DOI: 10.1063/5.0111131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/24/2022] [Indexed: 06/17/2023]
Abstract
Chaotic time series have been captured by reservoir computing models composed of a recurrent neural network whose output weights are trained in a supervised manner. These models, however, are typically limited to randomly connected networks of homogeneous units. Here, we propose a new class of structured reservoir models that incorporates a diversity of cell types and their known connections. In a first version of the model, the reservoir was composed of mean-rate units separated into pyramidal, parvalbumin, and somatostatin cells. Stability analysis of this model revealed two distinct dynamical regimes, namely, (i) an inhibition-stabilized network (ISN) where strong recurrent excitation is balanced by strong inhibition and (ii) a non-ISN network with weak excitation. These results were extended to a leaky integrate-and-fire model that captured different cell types along with their network architecture. ISN and non-ISN reservoir networks were trained to relay and generate a chaotic Lorenz attractor. Despite their increased performance, ISN networks operate in a regime of activity near the limits of stability where external perturbations yield a rapid divergence in output. The proposed framework of structured reservoir computing opens avenues for exploring how neural microcircuits can balance performance and stability when representing time series through distinct dynamical regimes.
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Affiliation(s)
- Jean-Philippe Thivierge
- University of Ottawa Brain and Mind Research Institute, 451 Smyth Rd., Ottawa, Ontario K1H 8M5, Canada
| | - Eloïse Giraud
- School of Psychology, University of Ottawa, 156 Jean-Jacques Lussier, Ottawa, Ontario K1N 6N5, Canada
| | - Michael Lynn
- University of Ottawa Brain and Mind Research Institute, 451 Smyth Rd., Ottawa, Ontario K1H 8M5, Canada
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4
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Chini M, Pfeffer T, Hanganu-Opatz I. An increase of inhibition drives the developmental decorrelation of neural activity. eLife 2022; 11:78811. [PMID: 35975980 PMCID: PMC9448324 DOI: 10.7554/elife.78811] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/16/2022] [Indexed: 11/23/2022] Open
Abstract
Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are poorly understood. The developmental transition has important functional consequences, as the latter state is thought to allow for more efficient storage, retrieval, and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation-inhibition (E-I) ratio toward inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in mice mimicking the etiology of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.
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Affiliation(s)
- Mattia Chini
- Institute of Developmental Neurophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Pfeffer
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ileana Hanganu-Opatz
- Institute of Developmental Neurophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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5
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Stieger KC, Eles JR, Ludwig K, Kozai TDY. Intracortical microstimulation pulse waveform and frequency recruits distinct spatiotemporal patterns of cortical neuron and neuropil activation. J Neural Eng 2022; 19. [PMID: 35263736 DOI: 10.1088/1741-2552/ac5bf5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/09/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neural prosthetics often use intracortical microstimulation (ICMS) for sensory restoration. To restore natural and functional feedback, we must first understand how stimulation parameters influence the recruitment of neural populations. ICMS waveform asymmetry modulates the spatial activation of neurons around an electrode at 10 Hz; however, it is unclear how asymmetry may differentially modulate population activity at frequencies typically employed in the clinic (e.g. 100 Hz). We hypothesized that stimulation waveform asymmetry would differentially modulate preferential activation of certain neural populations, and the differential population activity would be frequency-dependent. APPROACH We quantified how asymmetric stimulation waveforms delivered at 10 Hz or 100 Hz for 30s modulated spatiotemporal activity of cortical layer II/III pyramidal neurons using in vivo two-photon and mesoscale calcium imaging in anesthetized mice. Asymmetry is defined in terms of the ratio of the duration of the leading phase to the duration of the return phase of charge-balanced cathodal- and anodal-first waveforms (i.e. longer leading phase relative to return has larger asymmetry). MAIN RESULTS Neurons within 40-60µm of the electrode display stable stimulation-induced activity indicative of direct activation, which was independent of waveform asymmetry. The stability of 72% of activated neurons and the preferential activation of 20-90 % of neurons depended on waveform asymmetry. Additionally, this asymmetry-dependent activation of different neural populations was associated with differential progression of population activity. Specifically, neural activity tended to increase over time during 10 hz stimulation for some waveforms, whereas activity remained at the same level throughout stimulation for other waveforms. During 100 Hz stimulation, neural activity decreased over time for all waveforms, but decreased more for the waveforms that resulted in increasing neural activity during 10 Hz stimulation. SIGNIFICANCE These data demonstrate that at frequencies commonly used for sensory restoration, stimulation waveform alters the pattern of activation of different but overlapping populations of excitatory neurons. The impact of these waveform specific responses on the activation of different subtypes of neurons as well as sensory perception merits further investigation.
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Affiliation(s)
- Kevin C Stieger
- Bioengineering, University of Pittsburgh, 300 Technology Dr, Pittsburgh, Pennsylvania, 15219, UNITED STATES
| | - James Regis Eles
- Department of Bioengineering, University of Pittsburgh, 300 Technology Dr, Pittsburgh, Pennsylvania, 15219, UNITED STATES
| | - Kip Ludwig
- Biomedical Engineering and Neurological Surgery, University of Wisconsin Madison, XXX, Madison, Wisconsin, 53706, UNITED STATES
| | - Takashi D Yoshida Kozai
- Department of Bioengineering, University of Pittsburgh, 3501 Fifth Ave, 5059-BST3, Pittsburgh, PA 15213, USA, Pittsburgh, Pennsylvania, 15219, UNITED STATES
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Huang C. Modulation of the dynamical state in cortical network models. Curr Opin Neurobiol 2021; 70:43-50. [PMID: 34403890 PMCID: PMC8688204 DOI: 10.1016/j.conb.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/18/2021] [Accepted: 07/14/2021] [Indexed: 11/29/2022]
Abstract
Cortical neural responses can be modulated by various factors, such as stimulus inputs and the behavior state of the animal. Understanding the circuit mechanisms underlying modulations of network dynamics is important to understand the flexibility of circuit computations. Identifying the dynamical state of a network is an important first step to predict network responses to external stimulus and top-down modulatory inputs. Models in stable or unstable dynamical regimes require different analytic tools to estimate the network responses to inputs and the structure of neural variability. In this article, I review recent cortical models of state-dependent responses and their predictions about the underlying modulatory mechanisms.
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Affiliation(s)
- Chengcheng Huang
- Departments of Neuroscience and Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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7
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Prince LY, Tran MM, Grey D, Saad L, Chasiotis H, Kwag J, Kohl MM, Richards BA. Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information. Commun Biol 2021; 4:935. [PMID: 34354206 PMCID: PMC8342442 DOI: 10.1038/s42003-021-02437-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 07/09/2021] [Indexed: 11/30/2022] Open
Abstract
Neurons can carry information with both the synchrony and rate of their spikes. However, it is unknown whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, or vice versa. Here, we address this question using patterned optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. We used optical stimulation in layer 2/3 to encode a 1-bit signal using either the synchrony or rate of activity. We then examined the mutual information between this signal and the interneuron responses. We found that for a synchrony encoding, FS interneurons carried more information in the first five milliseconds, while both interneuron subtypes carried more information than excitatory neurons in later responses. For a rate encoding, we found that RS interneurons carried more information after several milliseconds. These data demonstrate that distinct interneuron subtypes in the neocortex have distinct sensitivities to synchrony versus rate codes. In order to address whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, Prince et al. used optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. They demonstrated that FS and RS interneurons had differential sensitivity to changes in synchrony and rate, which advances our understanding of neural processing in the neocortex.
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Affiliation(s)
- Luke Y Prince
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada.,School of Computer Science, McGill University, Montreal, QC, Canada.,Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Matthew M Tran
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Dorian Grey
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Lydia Saad
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Helen Chasiotis
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Jeehyun Kwag
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Michael M Kohl
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Blake A Richards
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada. .,School of Computer Science, McGill University, Montreal, QC, Canada. .,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada. .,Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
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8
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Bansal H, Gupta N, Roy S. Theoretical analysis of optogenetic spiking with ChRmine, bReaChES and CsChrimson-expressing neurons for retinal prostheses. J Neural Eng 2021; 18. [PMID: 34229315 DOI: 10.1088/1741-2552/ac1175] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 07/06/2021] [Indexed: 01/10/2023]
Abstract
Objective.Optogenetics has emerged as a promising technique for neural prosthetics, especially retinal prostheses, with unprecedented spatiotemporal resolution. Newly discovered opsins with high light sensitivity and fast temporal kinetics can provide sufficient temporal resolution at safe light powers and overcome the limitations of presently used opsins. It is also important to formulate accurate mathematical models for optogenetic retinal prostheses, which can facilitate optimization of photostimulation factors to improve the performance.Approach.A detailed theoretical analysis of optogenetic excitation of model retinal ganglion neurons (RGNs) and hippocampal neurons expressed with already tested opsins for retinal prostheses, namely, ChR2, ReaChR and ChrimsonR, and also with recently discovered potent opsins CsChrimson, bReaChES and ChRmine, was carried out.Main results.Under continuous illumination, ChRmine-expressing RGNs begin to respond at very low irradiances ∼10-4mW mm-2, and evoke firing upto ∼280 Hz, highest among other opsin-expressing RGNs, at 10-2mW mm-2. Under pulsed illumination at randomized photon fluxes, ChRmine-expressing RGNs respond to changes in pulse to pulse irradiances upto four logs, although very bright pulses >1014photons mm-2s-1block firing in these neurons. The minimum irradiance threshold for ChRmine-expressing RGNs is lower by two orders of magnitude, whereas, the first spike latency in ChRmine-expressing RGNs is shorter by an order of magnitude, alongwith stable latency of subsequest spikes compared to others. Further, a good set of photostimulation parameters were determined to achieve high-frequency control with single spike resolution at minimal power. Although ChrimsonR enables spiking upto 100 Hz in RGNs, it requires very high irradiances. ChRmine provides control at light powers that are two orders of magnitude smaller than that required with experimentally studied opsins, while maintaining single spike temporal resolution upto 40 Hz.Significance.The present study highlights the importance of ChRmine as a potential opsin for optogenetic retinal prostheses.
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Affiliation(s)
- Himanshu Bansal
- Department of Physics and Computer Science, Dayalbagh Educational Institute, Agra 282005, India
| | - Neha Gupta
- Department of Physics and Computer Science, Dayalbagh Educational Institute, Agra 282005, India
| | - Sukhdev Roy
- Department of Physics and Computer Science, Dayalbagh Educational Institute, Agra 282005, India
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9
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Antolik J, Sabatier Q, Galle C, Frégnac Y, Benosman R. Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1. Sci Rep 2021; 11:10783. [PMID: 34031442 PMCID: PMC8144184 DOI: 10.1038/s41598-021-88960-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 04/01/2021] [Indexed: 02/04/2023] Open
Abstract
The neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation patterns of the optogenetic matrix stimulator. We then quantify the relationship between spatial configuration of the imposed light pattern and the induced cortical activity. Our simulations in the absence of visual drive (simulated blindness) show that optogenetic stimulation with a spatial resolution as low as 100 [Formula: see text]m, and light intensity as weak as [Formula: see text] photons/s/cm[Formula: see text] is sufficient to evoke activity patterns in V1 close to those evoked by normal vision.
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Affiliation(s)
- Jan Antolik
- Faculty of Mathematics and Physics, Charles University, Malostranské nám. 25, 118 00, Prague 1, Czechia.
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France.
| | - Quentin Sabatier
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France
| | - Charlie Galle
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France
| | - Yves Frégnac
- Unité de Neurosciences, Information et Complexité (UNIC), NeuroPSI, Gif-sur-Yvette, France
| | - Ryad Benosman
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France
- University of Pittsburgh, McGowan Institute, 3025 E Carson St, Pittsburgh, PA, USA
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10
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Kim CM, Chow CC. Training Spiking Neural Networks in the Strong Coupling Regime. Neural Comput 2021; 33:1199-1233. [PMID: 34496392 DOI: 10.1162/neco_a_01379] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/23/2020] [Indexed: 11/04/2022]
Abstract
Recurrent neural networks trained to perform complex tasks can provide insight into the dynamic mechanism that underlies computations performed by cortical circuits. However, due to a large number of unconstrained synaptic connections, the recurrent connectivity that emerges from network training may not be biologically plausible. Therefore, it remains unknown if and how biological neural circuits implement dynamic mechanisms proposed by the models. To narrow this gap, we developed a training scheme that, in addition to achieving learning goals, respects the structural and dynamic properties of a standard cortical circuit model: strongly coupled excitatory-inhibitory spiking neural networks. By preserving the strong mean excitatory and inhibitory coupling of initial networks, we found that most of trained synapses obeyed Dale's law without additional constraints, exhibited large trial-to-trial spiking variability, and operated in inhibition-stabilized regime. We derived analytical estimates on how training and network parameters constrained the changes in mean synaptic strength during training. Our results demonstrate that training recurrent neural networks subject to strong coupling constraints can result in connectivity structure and dynamic regime relevant to cortical circuits.
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Affiliation(s)
- Christopher M Kim
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases/National Institutes of Health, Bethesda, MD 20814, U.S.A.
| | - Carson C Chow
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases/National Institutes of Health, Bethesda, MD 20814, U.S.A.
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11
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Nüst D, Eglen SJ. CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility. F1000Res 2021; 10:253. [PMID: 34367614 PMCID: PMC8311796 DOI: 10.12688/f1000research.51738.2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/15/2021] [Indexed: 11/20/2022] Open
Abstract
The traditional scientific paper falls short of effectively communicating computational research. To help improve this situation, we propose a system by which the computational workflows underlying research articles are checked. The CODECHECK system uses open infrastructure and tools and can be integrated into review and publication processes in multiple ways. We describe these integrations along multiple dimensions (importance, who, openness, when). In collaboration with academic publishers and conferences, we demonstrate CODECHECK with 25 reproductions of diverse scientific publications. These CODECHECKs show that asking for reproducible workflows during a collaborative review can effectively improve executability. While CODECHECK has clear limitations, it may represent a building block in Open Science and publishing ecosystems for improving the reproducibility, appreciation, and, potentially, the quality of non-textual research artefacts. The CODECHECK website can be accessed here: https://codecheck.org.uk/.
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Affiliation(s)
- Daniel Nüst
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | - Stephen J. Eglen
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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12
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Nüst D, Eglen SJ. CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility. F1000Res 2021; 10:253. [PMID: 34367614 PMCID: PMC8311796 DOI: 10.12688/f1000research.51738.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/22/2021] [Indexed: 11/08/2023] Open
Abstract
The traditional scientific paper falls short of effectively communicating computational research. To help improve this situation, we propose a system by which the computational workflows underlying research articles are checked. The CODECHECK system uses open infrastructure and tools and can be integrated into review and publication processes in multiple ways. We describe these integrations along multiple dimensions (importance, who, openness, when). In collaboration with academic publishers and conferences, we demonstrate CODECHECK with 25 reproductions of diverse scientific publications. These CODECHECKs show that asking for reproducible workflows during a collaborative review can effectively improve executability. While CODECHECK has clear limitations, it may represent a building block in Open Science and publishing ecosystems for improving the reproducibility, appreciation, and, potentially, the quality of non-textual research artefacts. The CODECHECK website can be accessed here: https://codecheck.org.uk/.
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Affiliation(s)
- Daniel Nüst
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | - Stephen J. Eglen
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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13
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Morrison CL, Greenwood PE, Ward LM. Plastic systemic inhibition controls amplitude while allowing phase pattern in a stochastic neural field model. Phys Rev E 2021; 103:032311. [PMID: 33862754 DOI: 10.1103/physreve.103.032311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 02/19/2021] [Indexed: 11/07/2022]
Abstract
We investigate oscillatory phase pattern formation and amplitude control for a linearized stochastic neuron field model by simulating Mexican-hat-coupled stochastic processes. We find, for several choices of parameters, that spatial pattern formation in the temporal phases of the coupled processes occurs if and only if their amplitudes are allowed to grow unrealistically large. Stimulated by recent work on homeostatic inhibitory plasticity, we introduce static and plastic (adaptive) systemic inhibitory mechanisms to keep the amplitudes stochastically bounded. We find that systems with static inhibition exhibited bounded amplitudes but no sustained phase patterns. With plastic systemic inhibition, on the other hand, the resulting systems exhibit both bounded amplitudes and sustained phase patterns. These results demonstrate that plastic inhibitory mechanisms in neural field models can dynamically control amplitudes while allowing patterns of phase synchronization to develop. Similar mechanisms of plastic systemic inhibition could play a role in regulating oscillatory functioning in the brain.
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Affiliation(s)
- Conor L Morrison
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
| | - Priscilla E Greenwood
- Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z2
| | - Lawrence M Ward
- Department of Psychology and Djavad Mowafaghian Centre for Brain Health, 2136 West Mall, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
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14
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Ye W. Dynamics of a Large-Scale Spiking Neural Network with Quadratic Integrate-and-Fire Neurons. Neural Plast 2021; 2021:6623926. [PMID: 33679968 PMCID: PMC7925051 DOI: 10.1155/2021/6623926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 01/18/2021] [Accepted: 02/05/2021] [Indexed: 11/17/2022] Open
Abstract
Since the high dimension and complexity of the large-scale spiking neural network, it is difficult to research the network dynamics. In recent decades, the mean-field approximation has been a useful method to reduce the dimension of the network. In this study, we construct a large-scale spiking neural network with quadratic integrate-and-fire neurons and reduce it to a mean-field model to research the network dynamics. We find that the activity of the mean-field model is consistent with the network activity. Based on this agreement, a two-parameter bifurcation analysis is performed on the mean-field model to understand the network dynamics. The bifurcation scenario indicates that the network model has the quiescence state, the steady state with a relatively high firing rate, and the synchronization state which correspond to the stable node, stable focus, and stable limit cycle of the system, respectively. There exist several stable limit cycles with different periods, so we can observe the synchronization states with different periods. Additionally, the model shows bistability in some regions of the bifurcation diagram which suggests that two different activities coexist in the network. The mechanisms that how these states switch are also indicated by the bifurcation curves.
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Affiliation(s)
- Weijie Ye
- School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou 510320, China
- Big data and Educational Statistics Application Laboratory, Guangdong University of Finance and Economics, Guangzhou 510320, China
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15
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Sadeh S, Clopath C. Inhibitory stabilization and cortical computation. Nat Rev Neurosci 2020; 22:21-37. [PMID: 33177630 DOI: 10.1038/s41583-020-00390-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2020] [Indexed: 12/22/2022]
Abstract
Neuronal networks with strong recurrent connectivity provide the brain with a powerful means to perform complex computational tasks. However, high-gain excitatory networks are susceptible to instability, which can lead to runaway activity, as manifested in pathological regimes such as epilepsy. Inhibitory stabilization offers a dynamic, fast and flexible compensatory mechanism to balance otherwise unstable networks, thus enabling the brain to operate in its most efficient regimes. Here we review recent experimental evidence for the presence of such inhibition-stabilized dynamics in the brain and discuss their consequences for cortical computation. We show how the study of inhibition-stabilized networks in the brain has been facilitated by recent advances in the technological toolbox and perturbative techniques, as well as a concomitant development of biologically realistic computational models. By outlining future avenues, we suggest that inhibitory stabilization can offer an exemplary case of how experimental neuroscience can progress in tandem with technology and theory to advance our understanding of the brain.
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Affiliation(s)
- Sadra Sadeh
- Bioengineering Department, Imperial College London, London, UK
| | - Claudia Clopath
- Bioengineering Department, Imperial College London, London, UK.
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16
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Dalgleish HWP, Russell LE, Packer AM, Roth A, Gauld OM, Greenstreet F, Thompson EJ, Häusser M. How many neurons are sufficient for perception of cortical activity? eLife 2020; 9:e58889. [PMID: 33103656 PMCID: PMC7695456 DOI: 10.7554/elife.58889] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/17/2020] [Indexed: 01/12/2023] Open
Abstract
Many theories of brain function propose that activity in sparse subsets of neurons underlies perception and action. To place a lower bound on the amount of neural activity that can be perceived, we used an all-optical approach to drive behaviour with targeted two-photon optogenetic activation of small ensembles of L2/3 pyramidal neurons in mouse barrel cortex while simultaneously recording local network activity with two-photon calcium imaging. By precisely titrating the number of neurons stimulated, we demonstrate that the lower bound for perception of cortical activity is ~14 pyramidal neurons. We find a steep sigmoidal relationship between the number of activated neurons and behaviour, saturating at only ~37 neurons, and show this relationship can shift with learning. Furthermore, activation of ensembles is balanced by inhibition of neighbouring neurons. This surprising perceptual sensitivity in the face of potent network suppression supports the sparse coding hypothesis, and suggests that cortical perception balances a trade-off between minimizing the impact of noise while efficiently detecting relevant signals.
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Affiliation(s)
- Henry WP Dalgleish
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
| | - Lloyd E Russell
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
| | - Adam M Packer
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
| | - Arnd Roth
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
| | - Oliver M Gauld
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
| | - Francesca Greenstreet
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
| | - Emmett J Thompson
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College LondonLondonUnited Kingdom
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17
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Sanzeni A, Histed MH. Finding patterns in cortical responses. eLife 2020; 9:e56234. [PMID: 32321626 PMCID: PMC7180050 DOI: 10.7554/elife.56234] [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: 04/09/2020] [Accepted: 04/09/2020] [Indexed: 11/13/2022] Open
Abstract
Simulations predict a paradoxical effect that should be revealed by patterned stimulation of the cortex.
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Affiliation(s)
| | - Mark H Histed
- Intramural Research Program, National Institute of Mental HealthBethesdaUnited States
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18
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Patil P, Yizhar O. In Vivo Optophysiology Reveals Lateral Inhibition among Layer 1 Interneurons. Neuron 2020; 106:14-16. [PMID: 32272063 DOI: 10.1016/j.neuron.2020.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Neurons in neocortical layer 1 (L1) are thought to regulate attentional processes through integration of long-range inputs and disinhibitory effects on the underlying cortex. A new study combines genetically targeted voltage imaging and optogenetics to elucidate the input-output transformations of the L1 network in the mouse somatosensory cortex, revealing unique features of sensory-evoked dynamics in L1 neurons.
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Affiliation(s)
- Pritish Patil
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Ofer Yizhar
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel.
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