1
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Redman WT, Acosta-Mendoza S, Wei XX, Goard MJ. Robust variability of grid cell properties within individual grid modules enhances encoding of local space. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582373. [PMID: 38915504 PMCID: PMC11195105 DOI: 10.1101/2024.02.27.582373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Although grid cells are one of the most well studied functional classes of neurons in the mammalian brain, the assumption that there is a single grid orientation and spacing per grid module has not been carefully tested. We investigate and analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the ability of encoding local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that variability, of a similar magnitude to the analyzed data, leads to significantly decreased decoding error, even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.
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Affiliation(s)
- William T Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara
- Intelligent Systems Center, Johns Hopkins University Applied Physics Lab
| | - Santiago Acosta-Mendoza
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara
| | - Xue-Xin Wei
- Department of Neuroscience, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
| | - Michael J Goard
- Department of Psychological and Brain Sciences, University of California, Santa Barbara
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara
- Neuroscience Research Institute, University of California Santa Barbara
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2
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El Srouji L, Abdelghany M, Ambethkar HR, Lee YJ, Berkay On M, Yoo SJB. Perspective: an optoelectronic future for heterogeneous, dendritic computing. Front Neurosci 2024; 18:1394271. [PMID: 38699677 PMCID: PMC11064649 DOI: 10.3389/fnins.2024.1394271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/05/2024] Open
Abstract
With the increasing number of applications reliant on large neural network models, the pursuit of more suitable computing architectures is becoming increasingly relevant. Progress toward co-integrated silicon photonic and CMOS circuits provides new opportunities for computing architectures with high bandwidth optical networks and high-speed computing. In this paper, we discuss trends in neuromorphic computing architecture and outline an optoelectronic future for heterogeneous, dendritic neuromorphic computing.
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Affiliation(s)
| | | | | | | | | | - S. J. Ben Yoo
- Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, United States
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3
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Costa F, Schaft EV, Huiskamp G, Aarnoutse EJ, Van't Klooster MA, Krayenbühl N, Ramantani G, Zijlmans M, Indiveri G, Sarnthein J. Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework. Nat Commun 2024; 15:3255. [PMID: 38627406 PMCID: PMC11021517 DOI: 10.1038/s41467-024-47495-y] [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: 11/10/2023] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman's ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.
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Affiliation(s)
- Filippo Costa
- Klinik für Neurochirurgie, Universitätsspital Zürich und Universität Zürich, Zürich, Switzerland.
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Eline V Schaft
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Geertjan Huiskamp
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maryse A Van't Klooster
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Niklaus Krayenbühl
- Division of Pediatric Neurosurgery, University Children's Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- Division of Pediatric Neurosurgery, University Children's Hospital Zurich and University of Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital Zürich und Universität Zürich, Zürich, Switzerland.
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland.
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4
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Mohammadi M, Carriot J, Mackrous I, Cullen KE, Chacron MJ. Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion. PLoS Biol 2024; 22:e3002623. [PMID: 38687807 PMCID: PMC11086886 DOI: 10.1371/journal.pbio.3002623] [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: 06/13/2023] [Revised: 05/10/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.
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Affiliation(s)
- Mohammad Mohammadi
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Jerome Carriot
- Department of Physiology, McGill University, Montreal, Canada
| | | | - Kathleen E. Cullen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
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5
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Wang H, Lu Y, Liu S, Yu J, Hu M, Li S, Yang R, Watanabe K, Taniguchi T, Ma Y, Miao X, Zhuge F, He Y, Zhai T. Adaptive Neural Activation and Neuromorphic Processing via Drain-Injection Threshold-Switching Float Gate Transistor Memory. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2309099. [PMID: 37953691 DOI: 10.1002/adma.202309099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Hetero-modulated neural activation is vital for adaptive information processing and learning that occurs in brain. To implement brain-inspired adaptive processing, previously various neurotransistors oriented for synaptic functions are extensively explored, however, the emulation of nonlinear neural activation and hetero-modulated behaviors are not possible due to the lack of threshold switching behavior in a conventional transistor structure. Here, a 2D van der Waals float gate transistor (FGT) that exhibits steep threshold switching behavior, and the emulation of hetero-modulated neuron functions (integrate-and-fire, sigmoid type activation) for adaptive sensory processing, are reported. Unlike conventional FGTs, the threshold switching behavior stems from impact ionization in channel and the coupled charge injection to float gate. When a threshold is met, a sub-30 mV dec-1 increase of transistor conductance by more than four orders is triggered with a typical switch time of approximately milliseconds. Essentially, by feeding light sensing signal as the modulation input, it is demonstrated that two typical tasks that rely on adaptive neural activation, including collision avoidance and adaptive visual perception, can be realized. These results may shed light on the emulation of rich hetero-modulating behaviors in biological neurons and the realization of biomimetic neuromorphic processing at low hardware cost.
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Affiliation(s)
- Han Wang
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Yuanlong Lu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Shangbo Liu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Jun Yu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Man Hu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Sainan Li
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Rui Yang
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Kenji Watanabe
- Research Center for Electronic and Optical Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, 305-0044, Japan
| | - Takashi Taniguchi
- Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Namiki, Tsukuba, 305-0044, Japan
| | - Ying Ma
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Xiangshui Miao
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Fuwei Zhuge
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Yuhui He
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
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6
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Arnaudon A, Reva M, Zbili M, Markram H, Van Geit W, Kanari L. Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. iScience 2023; 26:108222. [PMID: 37953946 PMCID: PMC10638024 DOI: 10.1016/j.isci.2023.108222] [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: 05/08/2023] [Revised: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/14/2023] Open
Abstract
Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.
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Affiliation(s)
- Alexis Arnaudon
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Maria Reva
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Mickael Zbili
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Lida Kanari
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
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7
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Arribas DM, Marin-Burgin A, Morelli LG. Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus. eLife 2023; 12:e80250. [PMID: 37584478 PMCID: PMC10476965 DOI: 10.7554/elife.80250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/15/2023] [Indexed: 08/17/2023] Open
Abstract
Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages in mouse hippocampal slices to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs' encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time-dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.
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Affiliation(s)
- Diego M Arribas
- Instituto de Investigacion en Biomedicina de Buenos Aires (IBioBA) – CONICET/Partner Institute of the Max Planck Society, Polo Cientifico TecnologicoBuenos AiresArgentina
| | - Antonia Marin-Burgin
- Instituto de Investigacion en Biomedicina de Buenos Aires (IBioBA) – CONICET/Partner Institute of the Max Planck Society, Polo Cientifico TecnologicoBuenos AiresArgentina
| | - Luis G Morelli
- Instituto de Investigacion en Biomedicina de Buenos Aires (IBioBA) – CONICET/Partner Institute of the Max Planck Society, Polo Cientifico TecnologicoBuenos AiresArgentina
- Departamento de Fisica, FCEyN UBA, Ciudad UniversitariaBuenos AiresArgentina
- Max Planck Institute for Molecular Physiology, Department of Systemic Cell BiologyDortmundGermany
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8
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Calhoun G, Chen CT, Kanold PO. Bilateral widefield calcium imaging reveals circuit asymmetries and lateralized functional activation of the mouse auditory cortex. Proc Natl Acad Sci U S A 2023; 120:e2219340120. [PMID: 37459544 PMCID: PMC10372568 DOI: 10.1073/pnas.2219340120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/29/2023] [Indexed: 07/20/2023] Open
Abstract
Coordinated functioning of the two cortical hemispheres is crucial for perception. The human auditory cortex (ACx) shows functional lateralization with the left hemisphere specialized for processing speech, whereas the right analyzes spectral content. In mice, virgin females demonstrate a left-hemisphere response bias to pup vocalizations that strengthens with motherhood. However, how this lateralized function is established is unclear. We developed a widefield imaging microscope to simultaneously image both hemispheres of mice to bilaterally monitor functional responses. We found that global ACx topography is symmetrical and stereotyped. In both male and virgin female mice, the secondary auditory cortex (A2) in the left hemisphere shows larger responses than right to high-frequency tones and adult vocalizations; however, only virgin female mice show a left-hemisphere bias in A2 in response to adult pain calls. These results indicate hemispheric bias with both sex-independent and -dependent aspects. Analyzing cross-hemispheric functional correlations showed that asymmetries exist in the strength of correlations between DM-AAF and A2-AAF, while other ACx areas showed smaller differences. We found that A2 showed lower cross-hemisphere correlation than other cortical areas, consistent with the lateralized functional activation of A2. Cross-hemispheric activity correlations are lower in deaf, otoferlin knockout (OTOF-/-) mice, indicating that the development of functional cross-hemispheric connections is experience dependent. Together, our results reveal that ACx is topographically symmetric at the macroscopic scale but that higher-order A2 shows sex-dependent and independent lateralized responses due to asymmetric intercortical functional connections. Moreover, our results suggest that sensory experience is required to establish functional cross-hemispheric connectivity.
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Affiliation(s)
- Georgia Calhoun
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD21205
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD21205
| | - Chih-Ting Chen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD21205
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD21205
| | - Patrick O. Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD21205
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD21205
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9
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Metzen MG, Chacron MJ. Descending pathways increase sensory neural response heterogeneity to facilitate decoding and behavior. iScience 2023; 26:107139. [PMID: 37416462 PMCID: PMC10320509 DOI: 10.1016/j.isci.2023.107139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/25/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
The functional role of heterogeneous spiking responses of otherwise similarly tuned neurons to stimulation, which has been observed ubiquitously, remains unclear to date. Here, we demonstrate that such response heterogeneity serves a beneficial function that is used by downstream brain areas to generate behavioral responses that follows the detailed timecourse of the stimulus. Multi-unit recordings from sensory pyramidal cells within the electrosensory system of Apteronotus leptorhynchus were performed and revealed highly heterogeneous responses that were similar for all cell types. By comparing the coding properties of a given neural population before and after inactivation of descending pathways, we found that heterogeneities were beneficial as decoding was then more robust to the addition of noise. Taken together, our results not only reveal that descending pathways actively promote response heterogeneity within a given cell type, but also uncover a beneficial function for such heterogeneity that is used by the brain to generate behavior.
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Affiliation(s)
- Michael G. Metzen
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Maurice J. Chacron
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
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10
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Hull JM, Denomme N, Yuan Y, Booth V, Isom LL. Heterogeneity of voltage gated sodium current density between neurons decorrelates spiking and suppresses network synchronization in Scn1b null mouse models. Sci Rep 2023; 13:8887. [PMID: 37264112 PMCID: PMC10235421 DOI: 10.1038/s41598-023-36036-0] [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: 02/06/2023] [Accepted: 05/28/2023] [Indexed: 06/03/2023] Open
Abstract
Voltage gated sodium channels (VGSCs) are required for action potential initiation and propagation in mammalian neurons. As with other ion channel families, VGSC density varies between neurons. Importantly, sodium current (INa) density variability is reduced in pyramidal neurons of Scn1b null mice. Scn1b encodes the VGSC β1/ β1B subunits, which regulate channel expression, trafficking, and voltage dependent properties. Here, we investigate how variable INa density in cortical layer 6 and subicular pyramidal neurons affects spike patterning and network synchronization. Constitutive or inducible Scn1b deletion enhances spike timing correlations between pyramidal neurons in response to fluctuating stimuli and impairs spike-triggered average current pattern diversity while preserving spike reliability. Inhibiting INa with a low concentration of tetrodotoxin similarly alters patterning without impairing reliability, with modest effects on firing rate. Computational modeling shows that broad INa density ranges confer a similarly broad spectrum of spike patterning in response to fluctuating synaptic conductances. Network coupling of neurons with high INa density variability displaces the coupling requirements for synchronization and broadens the dynamic range of activity when varying synaptic strength and network topology. Our results show that INa heterogeneity between neurons potently regulates spike pattern diversity and network synchronization, expanding VGSC roles in the nervous system.
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Affiliation(s)
- Jacob M Hull
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Nicholas Denomme
- Department of Pharmacology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yukun Yuan
- Department of Pharmacology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Victoria Booth
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Mathematics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lori L Isom
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Pharmacology, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Neurology, University of Michigan, Ann Arbor, MI, 48109, USA.
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11
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Fabian JM, O'Carrol DC, Wiederman SD. Sparse spike trains and the limitation of rate codes underlying rapid behaviours. Biol Lett 2023; 19:20230099. [PMID: 37161293 PMCID: PMC10170213 DOI: 10.1098/rsbl.2023.0099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] Open
Abstract
Animals live in dynamic worlds where they use sensorimotor circuits to rapidly process information and drive behaviours. For example, dragonflies are aerial predators that react to movements of prey within tens of milliseconds. These pursuits are likely controlled by identified neurons in the dragonfly, which have well-characterized physiological responses to moving targets. Predominantly, neural activity in these circuits is interpreted in context of a rate code, where information is conveyed by changes in the number of spikes over a time period. However, such a description of neuronal activity is difficult to achieve in real-world, real-time scenarios. Here, we contrast a neuroscientists' post-hoc view of spiking activity with the information available to the animal in real-time. We describe how performance of a rate code is readily overestimated and outline a rate code's significant limitations in driving rapid behaviours.
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Affiliation(s)
- Joseph M Fabian
- School of Biomedicine, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | | | - Steven D Wiederman
- School of Biomedicine, The University of Adelaide, Adelaide, South Australia 5005, Australia
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12
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Chang JT, Fitzpatrick D. Development of visual response selectivity in cortical GABAergic interneurons. Nat Commun 2022; 13:3791. [PMID: 35778379 PMCID: PMC9249896 DOI: 10.1038/s41467-022-31284-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 06/09/2022] [Indexed: 11/09/2022] Open
Abstract
The visual cortex of carnivores and primates displays a high degree of modular network organization characterized by local clustering and structured long-range correlations of activity and functional properties. Excitatory networks display modular organization before the onset of sensory experience, but the developmental timeline for modular networks of GABAergic interneurons remains under-explored. Using in vivo calcium imaging of the ferret visual cortex, we find evidence that before visual experience, interneurons display weak orientation tuning and widespread, correlated activity in response to visual stimuli. Robust modular organization and orientation tuning are evident with as little as one week of visual experience. Furthermore, we find that the maturation of orientation tuning requires visual experience, while the reduction in widespread, correlated network activity does not. Thus, the maturation of inhibitory cortical networks occurs in a delayed, parallel process relative to excitatory neurons. Excitatory neurons in the carnivore and primate visual cortex display orientation selectivity arranged in a modular fashion before the onset of visual experience, but the developmental timeline for visual response selectivity of inhibitory neurons is unknown. Using in vivo calcium imaging in ferret visual cortex, the authors show that experience-dependent emergence of orientation selectivity develops in a modular fashion for inhibitory interneurons over the first week of visual experience.
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Affiliation(s)
- Jeremy T Chang
- Department of Functional Architecture and Development of Cerebral Cortex, Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA.
| | - David Fitzpatrick
- Department of Functional Architecture and Development of Cerebral Cortex, Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
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13
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Kim S, Roh H, Im M. Artificial Visual Information Produced by Retinal Prostheses. Front Cell Neurosci 2022; 16:911754. [PMID: 35734216 PMCID: PMC9208577 DOI: 10.3389/fncel.2022.911754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/18/2022] [Indexed: 11/18/2022] Open
Abstract
Numerous retinal prosthetic systems have demonstrated somewhat useful vision can be restored to individuals who had lost their sight due to outer retinal degenerative diseases. Earlier prosthetic studies have mostly focused on the confinement of electrical stimulation for improved spatial resolution and/or the biased stimulation of specific retinal ganglion cell (RGC) types for selective activation of retinal ON/OFF pathway for enhanced visual percepts. To better replicate normal vision, it would be also crucial to consider information transmission by spiking activities arising in the RGC population since an incredible amount of visual information is transferred from the eye to the brain. In previous studies, however, it has not been well explored how much artificial visual information is created in response to electrical stimuli delivered by microelectrodes. In the present work, we discuss the importance of the neural information for high-quality artificial vision. First, we summarize the previous literatures which have computed information transmission rates from spiking activities of RGCs in response to visual stimuli. Second, we exemplify a couple of studies which computed the neural information from electrically evoked responses. Third, we briefly introduce how information rates can be computed in the representative two ways – direct method and reconstruction method. Fourth, we introduce in silico approaches modeling artificial retinal neural networks to explore the relationship between amount of information and the spiking patterns. Lastly, we conclude our review with clinical implications to emphasize the necessity of considering visual information transmission for further improvement of retinal prosthetics.
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Affiliation(s)
- Sein Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
| | - Hyeonhee Roh
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
- School of Electrical Engineering, College of Engineering, Korea University, Seoul, South Korea
| | - Maesoon Im
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
- Division of Bio-Medical Science & Technology, KIST School, University of Science and Technology, Seoul, South Korea
- *Correspondence: Maesoon Im, ,
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14
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Parallel processing of sensory cue and spatial information in the dentate gyrus. Cell Rep 2022; 38:110257. [PMID: 35045280 PMCID: PMC8918037 DOI: 10.1016/j.celrep.2021.110257] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/28/2021] [Accepted: 12/21/2021] [Indexed: 01/03/2023] Open
Abstract
During exploration, animals form an internal map of an environment by combining information about landmarks and the animal's movement, a process that depends on the hippocampus. The dentate gyrus (DG) is the first stage of the hippocampal circuit where self-motion ("where") and sensory cue information ("what") are integrated, but it remains unknown how DG neurons encode this information during cognitive map formation. Using two-photon calcium imaging in mice running on a treadmill along with online cue manipulation, we identify robust sensory cue responses in DG granule cells. Cue cell responses are stable, stimulus-specific, and accompanied by inhibition of nearby neurons. This demonstrates the existence of "cue cells" in addition to better characterized "place cells" in the DG. We hypothesize that the DG supports parallel channels of spatial and non-spatial information that contribute distinctly to downstream computations and affect roles of the DG in spatial navigation and episodic memory.
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15
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Li Y, Kim R, Sejnowski TJ. Learning the Synaptic and Intrinsic Membrane Dynamics Underlying Working Memory in Spiking Neural Network Models. Neural Comput 2021; 33:3264-3287. [PMID: 34710902 PMCID: PMC8662709 DOI: 10.1162/neco_a_01409] [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: 11/03/2020] [Accepted: 03/15/2021] [Indexed: 12/12/2022]
Abstract
Recurrent neural network (RNN) models trained to perform cognitive tasks are a useful computational tool for understanding how cortical circuits execute complex computations. However, these models are often composed of units that interact with one another using continuous signals and overlook parameters intrinsic to spiking neurons. Here, we developed a method to directly train not only synaptic-related variables but also membrane-related parameters of a spiking RNN model. Training our model on a wide range of cognitive tasks resulted in diverse yet task-specific synaptic and membrane parameters. We also show that fast membrane time constants and slow synaptic decay dynamics naturally emerge from our model when it is trained on tasks associated with working memory (WM). Further dissecting the optimized parameters revealed that fast membrane properties are important for encoding stimuli, and slow synaptic dynamics are needed for WM maintenance. This approach offers a unique window into how connectivity patterns and intrinsic neuronal properties contribute to complex dynamics in neural populations.
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Affiliation(s)
- Yinghao Li
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A.
| | - Robert Kim
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, and Neurosciences Graduate Program and Medical Scientist Training Program, University of California San Diego, La Jolla, CA 92093, U.S.A.
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, and Institute for Neural Computation and Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, U.S.A.
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16
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Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM. Neural heterogeneity promotes robust learning. Nat Commun 2021; 12:5791. [PMID: 34608134 PMCID: PMC8490404 DOI: 10.1038/s41467-021-26022-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 09/10/2021] [Indexed: 11/24/2022] Open
Abstract
The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.
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Affiliation(s)
- Nicolas Perez-Nieves
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.
| | - Vincent C H Leung
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK
| | - Pier Luigi Dragotti
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK
| | - Dan F M Goodman
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.
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17
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Andrei AR, Debes S, Chelaru M, Liu X, Rodarte E, Spudich JL, Janz R, Dragoi V. Heterogeneous side effects of cortical inactivation in behaving animals. eLife 2021; 10:66400. [PMID: 34505577 PMCID: PMC8457825 DOI: 10.7554/elife.66400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 09/09/2021] [Indexed: 12/05/2022] Open
Abstract
Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These off-target side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey primary visual cortex (V1) induces robust suppression at the light-targeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified four types of stimulus-evoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. These results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Importantly, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side effects can confound interpretation of inactivation experiments.
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Affiliation(s)
- Ariana R Andrei
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Samantha Debes
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Mircea Chelaru
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Xiaoqin Liu
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Elsa Rodarte
- Department of Neurology, McGovern Medical School, University of Texas, Houston, United States
| | - John L Spudich
- Center for Membrane Biology, Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas, Houston, United States
| | - Roger Janz
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
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18
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Nassar MR, Scott D, Bhandari A. Noise Correlations for Faster and More Robust Learning. J Neurosci 2021; 41:6740-6752. [PMID: 34193556 PMCID: PMC8336712 DOI: 10.1523/jneurosci.3045-20.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/08/2021] [Accepted: 06/10/2021] [Indexed: 11/21/2022] Open
Abstract
Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented by stimulus-independent noise correlations that constrain learning to task-relevant dimensions. We test this idea in a set of neural networks that learn to perform a perceptual discrimination task. Correlations among similarly tuned units were manipulated independently of an overall population signal-to-noise ratio to test how the format of stored information affects learning. Higher noise correlations among similarly tuned units led to faster and more robust learning, favoring homogenous weights assigned to neurons within a functionally similar pool, and could emerge through Hebbian learning. When multiple discriminations were learned simultaneously, noise correlations across relevant feature dimensions sped learning, whereas those across irrelevant feature dimensions slowed it. Our results complement the existing theory on noise correlations by demonstrating that when such correlations are produced without significant degradation of the signal-to-noise ratio, they can improve the speed of readout learning by constraining it to appropriate dimensions.SIGNIFICANCE STATEMENT Positive noise correlations between similarly tuned neurons theoretically reduce the representational capacity of the brain, yet they are commonly observed, emerge dynamically in complex tasks, and persist even in well-trained animals. Here we show that such correlations, when embedded in a neural population with a fixed signal-to-noise ratio, can improve the speed and robustness with which an appropriate readout is learned. In a simple discrimination task such correlations can emerge naturally through Hebbian learning. In more complex tasks that require multiple discriminations, correlations between neurons that similarly encode the task-relevant feature improve learning by constraining it to the appropriate task dimension.
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Affiliation(s)
- Matthew R Nassar
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912-1821
- Department of Neuroscience, Brown University, Providence, Rhode Island 02912-1821
| | - Daniel Scott
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912-1821
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912-1821
| | - Apoorva Bhandari
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912-1821
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912-1821
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19
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Knoll G, Lindner B. Recurrence-mediated suprathreshold stochastic resonance. J Comput Neurosci 2021; 49:407-418. [PMID: 34003421 PMCID: PMC8556192 DOI: 10.1007/s10827-021-00788-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/21/2021] [Accepted: 04/26/2021] [Indexed: 11/29/2022]
Abstract
It has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.
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Affiliation(s)
- Gregory Knoll
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, Berlin, 10115, Germany. .,Physics Department of Humboldt University Berlin, Newtonstr. 15, 12489, Berlin, Germany.
| | - Benjamin Lindner
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, Berlin, 10115, Germany.,Physics Department of Humboldt University Berlin, Newtonstr. 15, 12489, Berlin, Germany
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20
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Environmental Enrichment Sharpens Sensory Acuity by Enhancing Information Coding in Barrel Cortex and Premotor Cortex. eNeuro 2021; 8:ENEURO.0309-20.2021. [PMID: 33893166 PMCID: PMC8143018 DOI: 10.1523/eneuro.0309-20.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 04/13/2021] [Accepted: 04/17/2021] [Indexed: 12/20/2022] Open
Abstract
Environmental enrichment (EE) is beneficial to sensory functions. Thus, elucidating the neural mechanism underlying improvement of sensory stimulus discrimination is important for developing therapeutic strategies. We aim to advance the understanding of such neural mechanism. We found that tactile enrichment improved tactile stimulus feature discrimination. The neural correlate of such improvement was revealed by analyzing single-cell information coding in both the primary somatosensory cortex and the premotor cortex of awake behaving animals. Our results show that EE enhances the decision-information coding capacity of cells that are tuned to adjacent whiskers, and of premotor cortical cells.
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21
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Da Costa L, Parr T, Sengupta B, Friston K. Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing. ENTROPY (BASEL, SWITZERLAND) 2021; 23:454. [PMID: 33921298 PMCID: PMC8069154 DOI: 10.3390/e23040454] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/06/2021] [Indexed: 02/07/2023]
Abstract
Active inference is a normative framework for explaining behaviour under the free energy principle-a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy-a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error-plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Biswa Sengupta
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
- Core Machine Learning Group, Zebra AI, London WC2H 8TJ, UK
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
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22
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Rhee JK, Park H, Kim T, Yamamoto Y, Tanaka-Yamamoto K. Projection-dependent heterogeneity of cerebellar granule cell calcium responses. Mol Brain 2021; 14:63. [PMID: 33789707 PMCID: PMC8011397 DOI: 10.1186/s13041-021-00773-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Cerebellar granule cells (GCs) relay mossy fiber (MF) inputs to Purkinje cell dendrites via their axons, the parallel fibers (PFs), which are individually located at a given sublayer of the molecular layer (ML). Although a certain degree of heterogeneity among GCs has been recently reported, variability of GC responses to MF inputs has never been associated with their most notable structural variability, location of their projecting PFs in the ML. Here, we utilize an adeno-associated virus (AAV)-mediated labeling technique that enables us to categorize GCs according to the location of their PFs, and compare the Ca2+ responses to MF stimulations between three groups of GCs, consisting of either GCs having PFs at the deep (D-GCs), middle (M-GCs), or superficial (S-GCs) sublayer. Our structural analysis revealed that there was no correlation between position of GC soma in the GC layer and location of its PF in the ML, confirming that our AAV-mediated labeling was important to test the projection-dependent variability of the Ca2+ responses in GCs. We then found that the Ca2+ responses of D-GCs differed from those of M-GCs. Pharmacological experiments implied that the different Ca2+ responses were mainly attributable to varied distributions of GABAA receptors (GABAARs) at the synaptic and extrasynaptic regions of GC dendrites. In addition to GABAAR distributions, amounts of extrasynaptic NMDA receptors appear to be also varied, because Ca2+ responses were different between D-GCs and M-GCs when glutamate spillover was enhanced. Whereas the Ca2+ responses of S-GCs were mostly equivalent to those of D-GCs and M-GCs, the blockade of GABA uptake resulted in larger Ca2+ responses in S-GCs compared with D-GCs and M-GCs, implying existence of mechanisms leading to more excitability in S-GCs with increased GABA release. Thus, this study reveals MF stimulation-mediated non-uniform Ca2+ responses in the cerebellar GCs associated with the location of their PFs in the ML, and raises a possibility that combination of inherent functional variability of GCs and their specific axonal projection contributes to the information processing through the GCs.
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Affiliation(s)
- Jun Kyu Rhee
- Center for Functional Connectomics, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.,Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology (UST), Seoul, 02792, Republic of Korea
| | - Heeyoun Park
- Center for Functional Connectomics, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Taegon Kim
- Center for Functional Connectomics, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.
| | - Yukio Yamamoto
- Center for Functional Connectomics, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.
| | - Keiko Tanaka-Yamamoto
- Center for Functional Connectomics, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.
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23
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See JZ, Homma NY, Atencio CA, Sohal VS, Schreiner CE. Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles. Sci Rep 2021; 11:4064. [PMID: 33603027 PMCID: PMC7893178 DOI: 10.1038/s41598-021-83565-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/18/2021] [Indexed: 01/31/2023] Open
Abstract
Neuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.
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Affiliation(s)
- Jermyn Z. See
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
| | - Natsumi Y. Homma
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
| | - Craig A. Atencio
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
| | - Vikaas S. Sohal
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,grid.266102.10000 0001 2297 6811Department of Psychiatry, University of California, San Francisco, USA
| | - Christoph E. Schreiner
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
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24
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Norris D, Yang P, Shin SY, Kearney AL, Kim HJ, Geddes T, Senior AM, Fazakerley DJ, Nguyen LK, James DE, Burchfield JG. Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression. iScience 2021; 24:102118. [PMID: 33659881 PMCID: PMC7892930 DOI: 10.1016/j.isci.2021.102118] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/07/2021] [Accepted: 01/22/2021] [Indexed: 12/12/2022] Open
Abstract
Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells. Insulin signaling is heterogeneous between cells in the same population The temporal response of signaling components within a cell is highly reproducible Upstream responses (Akt) can only partially predict downstream response (GLUT4) Protein expression variance is a driver of intercellular signaling heterogeneity
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Affiliation(s)
- Dougall Norris
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Pengyi Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Sung-Young Shin
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC 3800, Australia.,Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Alison L Kearney
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Hani Jieun Kim
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Thomas Geddes
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Alistair M Senior
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC 3800, Australia.,Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
| | - James G Burchfield
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
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25
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Kang JH, Jang YJ, Kim T, Lee BC, Lee SH, Im M. Electric Stimulation Elicits Heterogeneous Responses in ON but Not OFF Retinal Ganglion Cells to Transmit Rich Neural Information. IEEE Trans Neural Syst Rehabil Eng 2021; 29:300-309. [PMID: 33395394 DOI: 10.1109/tnsre.2020.3048973] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Retinal implants electrically stimulate surviving retinal neurons to restore vision in people blinded by outer retinal degeneration. Although the healthy retina is known to transmit a vast amount of visual information to the brain, it has not been studied whether prosthetic vision contains a similar amount of information. Here, we assessed the neural information transmitted by population responses arising in brisk transient (BT) and brisk sustained (BS) subtypes of ON and OFF retinal ganglion cells (RGCs) in the rabbit retina. To correlate the response heterogeneity and the information transmission, we first quantified the cell-to-cell heterogeneity by calculating the spike time tiling coefficient (STTC) across spiking patterns of RGCs in each type. Then, we computed the neural information encoded by the RGC population in a given type. In responses to light stimulation, spiking activities were more heterogeneous in OFF than ON RGCs (STTCAVG = 0.36, 0.45, 0.77 and 0.55 for OFF BT, OFF BS, ON BT, and ON BS, respectively). Interestingly, however, in responses to electric stimulation, both BT and BS subtypes of OFF RGCs showed remarkably homogeneous spiking patterns across cells (STTCAVG = 0.93 and 0.82 for BT and BS, respectively), whereas the two subtypes of ON RGCs showed slightly increased populational heterogeneity compared to light-evoked responses (STTCAVG = 0.71 and 0.63 for BT and BS, respectively). Consequently, the neural information encoded by the electrically-evoked responses of a population of 15 RGCs was substantially lower in the OFF than the ON pathway: OFF BT and BS cells transmit only ~23% and ~53% of the neural information transmitted by their ON counterparts. Together with previously-reported natural spiking activities in ON RGCs, the higher neural information may make ON responses more recognizable, eliciting the biased percepts of bright phosphenes.
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Wendling KP, Ly C. Statistical Analysis of Decoding Performances of Diverse Populations of Neurons. Neural Comput 2021; 33:764-801. [PMID: 33400901 DOI: 10.1162/neco_a_01355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
A central theme in computational neuroscience is determining the neural correlates of efficient and accurate coding of sensory signals. Diversity, or heterogeneity, of intrinsic neural attributes is known to exist in many brain areas and is thought to significantly affect neural coding. Recent theoretical and experimental work has argued that in uncoupled networks, coding is most accurate at intermediate levels of heterogeneity. Here we consider this question with data from in vivo recordings of neurons in the electrosensory system of weakly electric fish subject to the same realization of noisy stimuli; we use a generalized linear model (GLM) to assess the accuracy of (Bayesian) decoding of stimulus given a population spiking response. The long recordings enable us to consider many uncoupled networks and a relatively wide range of heterogeneity, as well as many instances of the stimuli, thus enabling us to address this question with statistical power. The GLM decoding is performed on a single long time series of data to mimic realistic conditions rather than using trial-averaged data for better model fits. For a variety of fixed network sizes, we generally find that the optimal levels of heterogeneity are at intermediate values, and this holds in all core components of GLM. These results are robust to several measures of decoding performance, including the absolute value of the error, error weighted by the uncertainty of the estimated stimulus, and the correlation between the actual and estimated stimulus. Although a quadratic fit to decoding performance as a function of heterogeneity is statistically significant, the result is highly variable with low R2 values. Taken together, intermediate levels of neural heterogeneity are indeed a prominent attribute for efficient coding even within a single time series, but the performance is highly variable.
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Affiliation(s)
- Kyle P Wendling
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, U.S.A.
| | - Cheng Ly
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, U.S.A.
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Moore B, Khang S, Francis JT. Noise-Correlation Is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates. Front Behav Neurosci 2020; 14:541920. [PMID: 33343308 PMCID: PMC7739882 DOI: 10.3389/fnbeh.2020.541920] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/30/2020] [Indexed: 11/17/2022] Open
Abstract
Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain-computer interfaces (BCIs; Zhao et al., 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearson's correlation coefficient to measure shared variability between simultaneously recorded units (32-112) and found significantly higher noise-correlation and positive correlation between the populations' signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted a mean matched noise-correlation analysis to decouple known interactions between event-triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however, the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning (RL) towards autonomous BCI updating.
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Affiliation(s)
- Brittany Moore
- Department of Biomedical Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
| | - Sheng Khang
- Department of Biomedical Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
| | - Joseph Thachil Francis
- Department of Biomedical Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
- Department of Electrical and Computer Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
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Pojoga SA, Kharas N, Dragoi V. Perceptually unidentifiable stimuli influence cortical processing and behavioral performance. Nat Commun 2020; 11:6109. [PMID: 33257683 PMCID: PMC7705662 DOI: 10.1038/s41467-020-19848-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 10/28/2020] [Indexed: 12/05/2022] Open
Abstract
Our daily behavior is dynamically influenced by conscious and unconscious processes. Although the neural bases of conscious experience have been extensively investigated over the past several decades, how unconscious information impacts neural circuitry and behavior remains unknown. Here, we recorded populations of neurons in macaque primary visual cortex (V1) to find that perceptually unidentifiable stimuli repeatedly presented in the absence of awareness are encoded by neural populations in a way that facilitates their future processing in the context of a behavioral task. Such exposure increases stimulus sensitivity and information encoded in cell populations, even though animals are unaware of stimulus identity. This phenomenon is consistent with a Hebbian mechanism underlying an increase in functional connectivity specifically for the neurons activated by subthreshold stimuli. This form of unsupervised adaptation may constitute a vestigial pre-attention system using the mere frequency of stimulus occurrence to change stimulus representations even when sensory inputs are perceptually invisible.
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Affiliation(s)
- Sorin A Pojoga
- Department of Neurobiology and Anatomy, McGovern Medical School, Univ. of Texas-Houston, Houston, TX, 77030, USA
| | - Natasha Kharas
- Department of Neurobiology and Anatomy, McGovern Medical School, Univ. of Texas-Houston, Houston, TX, 77030, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, Univ. of Texas-Houston, Houston, TX, 77030, USA.
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29
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Interaction of neuronal and network mechanisms on firing propagation in a feedforward network. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.05.088] [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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30
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Ma Z, Liu H, Komiyama T, Wessel R. Stability of motor cortex network states during learning-associated neural reorganizations. J Neurophysiol 2020; 124:1327-1342. [PMID: 32937084 DOI: 10.1152/jn.00061.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and premotor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.NEW & NOTEWORTHY The neural activity reorganizes throughout motor learning, but how this reorganization impacts the stability of network states is unclear. We used two-photon calcium imaging to investigate how the network states in layer 2/3 and layer 5 of forelimb motor and premotor cortex are modulated by motor learning. We show that motor cortex network states are layer-specific and constant regarding criticality during neural activity reorganization, and suggests that layer-specific constraints could be motivated by different functions.
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Affiliation(s)
- Zhengyu Ma
- Department of Physics, Washington University in St. Louis, Saint Louis, Missouri
| | - Haixin Liu
- Neurobiology Section and Department of Neuroscience, University of California San Diego, La Jolla, California
| | - Takaki Komiyama
- Neurobiology Section and Department of Neuroscience, University of California San Diego, La Jolla, California
| | - Ralf Wessel
- Department of Physics, Washington University in St. Louis, Saint Louis, Missouri
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31
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Im M, Kim SW. Neurophysiological and medical considerations for better-performing microelectronic retinal prostheses. J Neural Eng 2020; 17:033001. [PMID: 32329755 DOI: 10.1088/1741-2552/ab8ca9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Maesoon Im
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea. Division of Bio-Medical Science & Technology, KIST School, University of Science and Technology (UST), Seoul, Republic of Korea
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32
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Sun X, Bernstein MJ, Meng M, Rao S, Sørensen AT, Yao L, Zhang X, Anikeeva PO, Lin Y. Functionally Distinct Neuronal Ensembles within the Memory Engram. Cell 2020; 181:410-423.e17. [PMID: 32187527 PMCID: PMC7166195 DOI: 10.1016/j.cell.2020.02.055] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 10/31/2019] [Accepted: 02/26/2020] [Indexed: 10/24/2022]
Abstract
Memories are believed to be encoded by sparse ensembles of neurons in the brain. However, it remains unclear whether there is functional heterogeneity within individual memory engrams, i.e., if separate neuronal subpopulations encode distinct aspects of the memory and drive memory expression differently. Here, we show that contextual fear memory engrams in the mouse dentate gyrus contain functionally distinct neuronal ensembles, genetically defined by the Fos- or Npas4-dependent transcriptional pathways. The Fos-dependent ensemble promotes memory generalization and receives enhanced excitatory synaptic inputs from the medial entorhinal cortex, which we find itself also mediates generalization. The Npas4-dependent ensemble promotes memory discrimination and receives enhanced inhibitory drive from local cholecystokinin-expressing interneurons, the activity of which is required for discrimination. Our study provides causal evidence for functional heterogeneity within the memory engram and reveals synaptic and circuit mechanisms used by each ensemble to regulate the memory discrimination-generalization balance.
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Affiliation(s)
- Xiaochen Sun
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Molecular and Cellular Neuroscience Graduate Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Max J Bernstein
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Meizhen Meng
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Siyuan Rao
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Andreas T Sørensen
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Li Yao
- State Key Laboratory of Cognitive Neuroscience & Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaohui Zhang
- State Key Laboratory of Cognitive Neuroscience & Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Polina O Anikeeva
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yingxi Lin
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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33
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Cubero RJ, Marsili M, Roudi Y. Multiscale relevance and informative encoding in neuronal spike trains. J Comput Neurosci 2020; 48:85-102. [PMID: 31993923 PMCID: PMC7035307 DOI: 10.1007/s10827-020-00740-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 12/20/2019] [Accepted: 01/03/2020] [Indexed: 11/26/2022]
Abstract
Neuronal responses to complex stimuli and tasks can encompass a wide range of time scales. Understanding these responses requires measures that characterize how the information on these response patterns are represented across multiple temporal resolutions. In this paper we propose a metric - which we call multiscale relevance (MSR) - to capture the dynamical variability of the activity of single neurons across different time scales. The MSR is a non-parametric, fully featureless indicator in that it uses only the time stamps of the firing activity without resorting to any a priori covariate or invoking any specific structure in the tuning curve for neural activity. When applied to neural data from the mEC and from the ADn and PoS regions of freely-behaving rodents, we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded and significantly better than the set of neurons with high local variations in their interspike intervals. Given these results, we propose that the MSR can be used as a measure to rank and select neurons for their information content without the need to appeal to any a priori covariate.
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Affiliation(s)
- Ryan John Cubero
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- The Abdus Salam International Center for Theoretical Physics, Trieste, Italy.
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy.
- IST Austria, Klosterneuburg, Austria.
| | - Matteo Marsili
- The Abdus Salam International Center for Theoretical Physics, Trieste, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Trieste, Italy
| | - Yasser Roudi
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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Mishra P, Narayanan R. Heterogeneities in intrinsic excitability and frequency-dependent response properties of granule cells across the blades of the rat dentate gyrus. J Neurophysiol 2020; 123:755-772. [PMID: 31913748 PMCID: PMC7052640 DOI: 10.1152/jn.00443.2019] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
The dentate gyrus (DG), the input gate to the hippocampus proper, is anatomically segregated into three different sectors, namely, the suprapyramidal blade, the crest region, and the infrapyramidal blade. Although there are well-established differences between these sectors in terms of neuronal morphology, connectivity patterns, and activity levels, differences in electrophysiological properties of granule cells within these sectors have remained unexplored. Here, employing somatic whole cell patch-clamp recordings from the rat DG, we demonstrate that granule cells in these sectors manifest considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, these neurons showed positive temporal summation of their responses to inputs mimicking excitatory postsynaptic currents and showed little to no sag in their voltage responses to pulse currents. Consistently, the impedance amplitude profile manifested low-pass characteristics and the impedance phase profile lacked positive phase values at all measured frequencies and voltages and for all sectors. Granule cells in all sectors exhibited class I excitability, with broadly linear firing rate profiles, and granule cells in the crest region fired significantly fewer action potentials compared with those in the infrapyramidal blade. Finally, we found weak pairwise correlations across the 18 different measurements obtained individually from each of the three sectors, providing evidence that these measurements are indeed reporting distinct aspects of neuronal physiology. Together, our analyses show that granule cells act as integrators of afferent information and emphasize the need to account for the considerable physiological heterogeneities in assessing their roles in information encoding and processing.NEW & NOTEWORTHY We employed whole cell patch-clamp recordings from granule cells in the three subregions of the rat dentate gyrus to demonstrate considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, granule cells did not express membrane potential resonance, and their impedance profiles lacked inductive phase leads at all measured frequencies. Our analyses also show that granule cells manifest class I excitability characteristics, categorizing them as integrators of afferent information.
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Affiliation(s)
- Poonam Mishra
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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35
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Idei H, Murata S, Yamashita Y, Ogata T. Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder. Front Psychiatry 2020; 11:762. [PMID: 32903328 PMCID: PMC7434834 DOI: 10.3389/fpsyt.2020.00762] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 07/17/2020] [Indexed: 11/13/2022] Open
Abstract
Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. In particular, developmental learning aspects of symptoms and interactions with the physical environment remain largely unexplored in computational modeling studies, although a leading computational theory has posited associations between psychiatric symptoms and an unusual estimation of information uncertainty (precision), which is an essential aspect of the real world and is estimated through learning processes. Here, we propose a mechanistic explanation that unifies the disparate observations via a hierarchical predictive coding and developmental learning framework, which is demonstrated in experiments using a neural network-controlled robot. The results show that, through the developmental learning process, homogeneous intrinsic neuronal excitability at the neural level induced via self-organization changes at the information processing level, such as hyper sensory precision and overfitting to sensory noise. These changes led to multifaceted alterations at the behavioral level, such as inflexibility, reduced generalization, and motor clumsiness. In addition, these behavioral alterations were accompanied by fluctuating neural activity and excessive development of synaptic connections. These findings might bridge various levels of understandings in autism spectrum and other neurodevelopmental disorders and provide insights into the disease processes underlying observed behaviors and brain activities in individual patients. This study shows the potential of neurorobotics frameworks for modeling how psychiatric disorders arise from dynamic interactions among the brain, body, and uncertain environments.
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Affiliation(s)
- Hayato Idei
- Department of Intermedia Studies, Waseda University, Tokyo, Japan
| | - Shingo Murata
- Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tetsuya Ogata
- Department of Intermedia Art and Science, Waseda University, Tokyo, Japan
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36
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Cai H, Dent ML. Best sensitivity of temporal modulation transfer functions in laboratory mice matches the amplitude modulation embedded in vocalizations. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2020; 147:337. [PMID: 32006990 PMCID: PMC7043865 DOI: 10.1121/10.0000583] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 12/18/2019] [Accepted: 12/22/2019] [Indexed: 06/10/2023]
Abstract
The perception of spectrotemporal changes is crucial for distinguishing between acoustic signals, including vocalizations. Temporal modulation transfer functions (TMTFs) have been measured in many species and reveal that the discrimination of amplitude modulation suffers at rapid modulation frequencies. TMTFs were measured in six CBA/CaJ mice in an operant conditioning procedure, where mice were trained to discriminate an 800 ms amplitude modulated white noise target from a continuous noise background. TMTFs of mice show a bandpass characteristic, with an upper limit cutoff frequency of around 567 Hz. Within the measured modulation frequencies ranging from 5 Hz to 1280 Hz, the mice show a best sensitivity for amplitude modulation at around 160 Hz. To look for a possible parallel evolution between sound perception and production in living organisms, we also analyzed the components of amplitude modulations embedded in natural ultrasonic vocalizations (USVs) emitted by this strain. We found that the cutoff frequency of amplitude modulation in most of the individual USVs is around their most sensitive range obtained from the psychoacoustic experiments. Further analyses of the duration and modulation frequency ranges of USVs indicated that the broader the frequency ranges of amplitude modulation in natural USVs, the shorter the durations of the USVs.
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Affiliation(s)
- Huaizhen Cai
- Department of Psychology, University at Buffalo-SUNY, Buffalo, New York 14260, USA
| | - Micheal L Dent
- Department of Psychology, University at Buffalo-SUNY, Buffalo, New York 14260, USA
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37
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Marhl U, Gosak M. Proper spatial heterogeneities expand the regime of scale-free behavior in a lattice of excitable elements. Phys Rev E 2019; 100:062203. [PMID: 31962506 DOI: 10.1103/physreve.100.062203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Indexed: 06/10/2023]
Abstract
Signatures of criticality, such as power law scaling of observables, have been empirically found in a plethora of real-life settings, including biological systems. The presence of critical states is believed to have many functional advantages and is associated with optimal operational abilities. Typically, critical dynamics arises in the proximity of phase transition points between absorbing disordered states (subcriticality) and ordered active regimes (supercriticality) and requires a high degree of fine tuning to emerge, which is unlikely to occur in real biological systems. In the present study we propose a rather simple, and biologically relevant mechanism that profoundly expands the critical-like region. In particular, by means of numerical simulation we show that incorporating spatial heterogeneities into the square lattice of map-based excitable oscillators broadens the parameter space in which the distribution of excitation wave sizes follows closely a power law. Most importantly, this behavior is only observed if the spatial profile exhibits intermediate-sized patches with similar excitability levels, whereas for large and small spatial clusters only marginal widening of the critical state is detected. Furthermore, it turned out that the presence of spatial disorder in general amplifies the size of excitation waves, whereby the relatively highest contributions are observed in the proximity of the critical point. We argue that the reported mechanism is of particular importance for excitable systems with local interactions between individual elements.
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Affiliation(s)
- Urban Marhl
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
- Institute of Mathematics, Physics and Mechanics, Jadranska ulica 19, SI-1000 Ljubljana, Slovenia
| | - Marko Gosak
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
- Institute of Physiology, Faculty of Medicine, University of Maribor, Taborska ulica 8, SI-2000 Maribor, Slovenia
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38
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Wang J, Cauwenberghs G, Broccard FD. Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics. IEEE Trans Biomed Eng 2019; 67:1831-1840. [PMID: 31647418 DOI: 10.1109/tbme.2019.2948809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties. METHODS Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data. RESULTS We reproduced the response properties of five different types of chemical synapses, including both excitatory ( AMPA, NMDA) and inhibitory ( GABAA, GABAC, glycine) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons. CONCLUSION Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips. SIGNIFICANCE A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.
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Sharpee TO, Berkowitz JA. Linking neural responses to behavior with information-preserving population vectors. Curr Opin Behav Sci 2019; 29:37-44. [PMID: 36590862 PMCID: PMC9802663 DOI: 10.1016/j.cobeha.2019.03.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
All systems for processing signals, both artificial and within animals, must obey fundamental statistical laws for how information can be processed. We discuss here recent results using information theory that provide a blueprint for building circuits where signals can be read-out without information loss. Many properties that are necessary to build information-preserving circuits are actually observed in real neurons, at least approximately. One such property is the use of logistic nonlinearity for relating inputs to neural response probability. Such nonlinearities are common in neural and intracellular networks. With this nonlinearity type, there is a linear combination of neural responses that is guaranteed to preserve Shannon information contained in the response of a neural population, no matter how many neurons it contains. This read-out measure is related to a classic quantity known as the population vector that has been quite successful in relating neural responses to animal behavior in a wide variety of cases. Nevertheless, the population vector did not withstand the scrutiny of detailed information-theoretical analyses that showed that it discards substantial amounts of information contained in the responses of a neural population. We discuss recent theoretical results showing how to modify the population vector expression to make it 'information-preserving', and what is necessary in terms of neural circuit organization to allow for lossless information transfer. Implementing these strategies within artificial systems is likely to increase their efficiency, especially for brain-machine interfaces.
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40
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Agetsuma M, Hamm JP, Tao K, Fujisawa S, Yuste R. Parvalbumin-Positive Interneurons Regulate Neuronal Ensembles in Visual Cortex. Cereb Cortex 2019; 28:1831-1845. [PMID: 29106504 DOI: 10.1093/cercor/bhx169] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 06/16/2017] [Indexed: 01/20/2023] Open
Abstract
For efficient cortical processing, neural circuit dynamics must be spatially and temporally regulated with great precision. Although parvalbumin-positive (PV) interneurons can control network synchrony, it remains unclear how they contribute to spatio-temporal patterning of activity. We investigated this by optogenetic inactivation of PV cells with simultaneous two-photon Ca2+ imaging from populations of neurons in mouse visual cortex in vivo. For both spontaneous and visually evoked activity, PV interneuron inactivation decreased network synchrony. But, interestingly, the response reliability and spatial extent of coactive neuronal ensembles during visual stimulation were also disrupted by PV-cell suppression, which reduced the functional repertoire of ensembles. Thus, PV interneurons can control the spatio-temporal dynamics of multineuronal activity by functionally sculpting neuronal ensembles and making them more different from each other. In doing so, inhibitory circuits could help to orthogonalize multicellular patterns of activity, enabling neural circuits to more efficiently occupy a higher dimensional space of potential dynamics.
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Affiliation(s)
- Masakazu Agetsuma
- Neurotechnology Center, Department of Biological Sciences, Columbia University, 550 West 120 Street, Box 4822, New York, NY 10027, USA.,Japan Science and Technology Agency, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan.,The Institute of Scientific and Industrial Research, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047, Japan.,National Institute for Physiological Sciences, Division of Homeostatic Development, 38 Nishigohnaka Myodaiji-cho, Okazaki, Aichi 444-8585, Japan
| | - Jordan P Hamm
- Neurotechnology Center, Department of Biological Sciences, Columbia University, 550 West 120 Street, Box 4822, New York, NY 10027, USA
| | - Kentaro Tao
- RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama351-0106, Japan
| | | | - Rafael Yuste
- Neurotechnology Center, Department of Biological Sciences, Columbia University, 550 West 120 Street, Box 4822, New York, NY 10027, USA
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41
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Robustness of respiratory rhythm generation across dynamic regimes. PLoS Comput Biol 2019; 15:e1006860. [PMID: 31361738 PMCID: PMC6697358 DOI: 10.1371/journal.pcbi.1006860] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 08/16/2019] [Accepted: 06/06/2019] [Indexed: 11/19/2022] Open
Abstract
A central issue in the study of the neural generation of respiratory rhythms is the role of the intrinsic pacemaking capabilities that some respiratory neurons exhibit. The debate on this issue has occurred in parallel to investigations of interactions among respiratory network neurons and how these contribute to respiratory behavior. In this computational study, we demonstrate how these two issues are inextricably linked. We use simulations and dynamical systems analysis to show that once a conditional respiratory pacemaker, which can be tuned across oscillatory and non-oscillatory dynamic regimes in isolation, is embedded into a respiratory network, its dynamics become masked: the network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, and that node's outputs are dominated by network influences. Furthermore, the outputs of the respiratory central pattern generator as a whole are invariant to these changes of dynamical properties, which ensures flexible and robust performance over a wide dynamic range.
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42
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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. Cortical microcircuits are highly inhomogeneous dynamical systems whose information processing capacity is determined by the characteristics of its heterogeneous components and their complex interactions. The high degree of variability that characterizes macroscopic population dynamics, both during ongoing, spontaneous activity and active processing states reflects the underlying complexity and heterogeneity which has the potential to dramatically constrain the space of functions that any given circuit can compute, leading to richer and more expressive information processing systems. In this study, we identify different tentative sources of heterogeneity and assess their differential and cumulative contribution to the microcircuit’s dynamics and information processing capacity. We study these properties in a generic Layer 2/3 cortical microcircuit model, built and constrained by multiple sources of experimental data, and demonstrate that heterogeneity in neuronal properties and microconnectivity structure are important sources of functional specialization, greatly improving the circuit’s processing capacity, while capturing various important features of cortical physiology.
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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
- * E-mail:
| | - 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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43
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Berry Ii MJ, Lebois F, Ziskind A, da Silveira RA. Functional Diversity in the Retina Improves the Population Code. Neural Comput 2018; 31:270-311. [PMID: 30576618 DOI: 10.1162/neco_a_01158] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Within a given brain region, individual neurons exhibit a wide variety of different feature selectivities. Here, we investigated the impact of this extensive functional diversity on the population neural code. Our approach was to build optimal decoders to discriminate among stimuli using the spiking output of a real, measured neural population and compare its performance against a matched, homogeneous neural population with the same number of cells and spikes. Analyzing large populations of retinal ganglion cells, we found that the real, heterogeneous population can yield a discrimination error lower than the homogeneous population by several orders of magnitude and consequently can encode much more visual information. This effect increases with population size and with graded degrees of heterogeneity. We complemented these results with an analysis of coding based on the Chernoff distance, as well as derivations of inequalities on coding in certain limits, from which we can conclude that the beneficial effect of heterogeneity occurs over a broad set of conditions. Together, our results indicate that the presence of functional diversity in neural populations can enhance their coding fidelity appreciably. A noteworthy outcome of our study is that this effect can be extremely strong and should be taken into account when investigating design principles for neural circuits.
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Affiliation(s)
- Michael J Berry Ii
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Felix Lebois
- Department of Physics, Ecole Normale Supérieure, 75005 Paris, France
| | - Avi Ziskind
- Department of Physics, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Rava Azeredo da Silveira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, U.S.A.; Department of Physics, Ecole Normale Supérieure, 75005 Paris; Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University, 75231 Paris; Université Paris Diderot Sorbonne Paris Cité, 75031 Paris; Sorbonne Universités UPMC Université Paris 6, 75005 Paris, France; CNRS
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44
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Systematic Analysis of Transmitter Coexpression Reveals Organizing Principles of Local Interneuron Heterogeneity. eNeuro 2018; 5:eN-NWR-0212-18. [PMID: 30294668 PMCID: PMC6171738 DOI: 10.1523/eneuro.0212-18.2018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 09/07/2018] [Accepted: 09/13/2018] [Indexed: 01/02/2023] Open
Abstract
Broad neuronal classes are surprisingly heterogeneous across many parameters, and subclasses often exhibit partially overlapping traits including transmitter coexpression. However, the extent to which transmitter coexpression occurs in predictable, consistent patterns is unknown. Here, we demonstrate that pairwise coexpression of GABA and multiple neuropeptide families by olfactory local interneurons (LNs) of the moth Manduca sexta is highly heterogeneous, with a single LN capable of expressing neuropeptides from at least four peptide families and few instances in which neuropeptides are consistently coexpressed. Using computational modeling, we demonstrate that observed coexpression patterns cannot be explained by independent probabilities of expression of each neuropeptide. Our analyses point to three organizing principles that, once taken into consideration, allow replication of overall coexpression structure: (1) peptidergic neurons are highly likely to coexpress GABA; (2) expression probability of allatotropin depends on myoinhibitory peptide expression; and (3) the all-or-none coexpression patterns of tachykinin neurons with several other neuropeptides. For other peptide pairs, the presence of one peptide was not predictive of the presence of the other, and coexpression probability could be replicated by independent probabilities. The stochastic nature of these coexpression patterns highlights the heterogeneity of transmitter content among LNs and argues against clear-cut definition of subpopulation types based on the presence of single neuropeptides. Furthermore, the receptors for all neuropeptides and GABA were expressed within each population of principal neuron type in the antennal lobe (AL). Thus, activation of any given LN results in a dynamic cocktail of modulators that have the potential to influence every level of olfactory processing within the AL.
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45
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Harel Y, Meir R, Opper M. Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation. Neural Comput 2018; 30:2056-2112. [PMID: 29949463 DOI: 10.1162/neco_a_01105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Neural decoding may be formulated as dynamic state estimation (filtering) based on point-process observations, a generally intractable problem. Numerical sampling techniques are often practically useful for the decoding of real neural data. However, they are less useful as theoretical tools for modeling and understanding sensory neural systems, since they lead to limited conceptual insight into optimal encoding and decoding strategies. We consider sensory neural populations characterized by a distribution over neuron parameters. We develop an analytically tractable Bayesian approximation to optimal filtering based on the observation of spiking activity that greatly facilitates the analysis of optimal encoding in situations deviating from common assumptions of uniform coding. Continuous distributions are used to approximate large populations with few parameters, resulting in a filter whose complexity does not grow with population size and allowing optimization of population parameters rather than individual tuning functions. Numerical comparison with particle filtering demonstrates the quality of the approximation. The analytic framework leads to insights that are difficult to obtain from numerical algorithms and is consistent with biological observations about the distribution of sensory cells' preferred stimuli.
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Affiliation(s)
- Yuval Harel
- Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 320003, Israel
| | - Ron Meir
- Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 320003, Israel
| | - Manfred Opper
- Department of Electrical Engineering and Computer Science, Technical University Berlin, Berlin 10587, Germany
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46
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Mittal D, Narayanan R. Degeneracy in the robust expression of spectral selectivity, subthreshold oscillations, and intrinsic excitability of entorhinal stellate cells. J Neurophysiol 2018; 120:576-600. [PMID: 29718802 PMCID: PMC6101195 DOI: 10.1152/jn.00136.2018] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Biological heterogeneities are ubiquitous and play critical roles in the emergence of physiology at multiple scales. Although neurons in layer II (LII) of the medial entorhinal cortex (MEC) express heterogeneities in channel properties, the impact of such heterogeneities on the robustness of their cellular-scale physiology has not been assessed. Here, we performed a 55-parameter stochastic search spanning nine voltage- or calcium-activated channels to assess the impact of channel heterogeneities on the concomitant emergence of 10 in vitro electrophysiological characteristics of LII stellate cells (SCs). We generated 150,000 models and found a heterogeneous subpopulation of 449 valid models to robustly match all electrophysiological signatures. We employed this heterogeneous population to demonstrate the emergence of cellular-scale degeneracy in SCs, whereby disparate parametric combinations expressing weak pairwise correlations resulted in similar models. We then assessed the impact of virtually knocking out each channel from all valid models and demonstrate that the mapping between channels and measurements was many-to-many, a critical requirement for the expression of degeneracy. Finally, we quantitatively predict that the spike-triggered average of SCs should be endowed with theta-frequency spectral selectivity and coincidence detection capabilities in the fast gamma-band. We postulate this fast gamma-band coincidence detection as an instance of cellular-scale-efficient coding, whereby SC response characteristics match the dominant oscillatory signals in LII MEC. The heterogeneous population of valid SC models built here unveils the robust emergence of cellular-scale physiology despite significant channel heterogeneities, and forms an efficacious substrate for evaluating the impact of biological heterogeneities on entorhinal network function. NEW & NOTEWORTHY We assessed the impact of heterogeneities in channel properties on the robustness of cellular-scale physiology of medial entorhinal cortical stellate neurons. We demonstrate that neuronal models with disparate channel combinations were endowed with similar physiological characteristics, as a consequence of the many-to-many mapping between channel properties and the physiological characteristics that they modulate. We predict that the spike-triggered average of stellate cells should be endowed with theta-frequency spectral selectivity and fast gamma-band coincidence detection capabilities.
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Affiliation(s)
- Divyansh Mittal
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science , Bangalore , India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science , Bangalore , India
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47
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Meng J, Li Z, Li H, Zhu J, Yu H. The Common and Distinct Orientation Adaptation Effect at Pinwheel Centers in Areas 21a and 17 of Cats. Neuroscience 2018; 379:77-92. [DOI: 10.1016/j.neuroscience.2018.03.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 03/02/2018] [Accepted: 03/05/2018] [Indexed: 11/16/2022]
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48
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Gutnisky DA, Beaman C, Lew SE, Dragoi V. Cortical response states for enhanced sensory discrimination. eLife 2017; 6:29226. [PMID: 29274146 PMCID: PMC5760207 DOI: 10.7554/elife.29226] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Accepted: 12/21/2017] [Indexed: 11/20/2022] Open
Abstract
Brain activity during wakefulness is characterized by rapid fluctuations in neuronal responses. Whether these fluctuations play any role in modulating the accuracy of behavioral responses is poorly understood. Here, we investigated whether and how trial changes in the population response impact sensory coding in monkey V1 and perceptual performance. Although the responses of individual neurons varied widely across trials, many cells tended to covary with the local population. When population activity was in a ‘low’ state, neurons had lower evoked responses and correlated variability, yet higher probability to predict perceptual accuracy. The impact of firing rate fluctuations on network and perceptual accuracy was strongest 200 ms before stimulus presentation, and it greatly diminished when the number of cells used to measure the state of the population was decreased. These findings indicate that enhanced perceptual discrimination occurs when population activity is in a ‘silent’ response mode in which neurons increase information extraction.
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Affiliation(s)
- Diego A Gutnisky
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States.,Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Charles Beaman
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Sergio E Lew
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States.,Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Argentina, South America
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
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49
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Beiran M, Kruscha A, Benda J, Lindner B. Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations. J Comput Neurosci 2017; 44:189-202. [PMID: 29222729 DOI: 10.1007/s10827-017-0674-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 11/08/2017] [Accepted: 11/12/2017] [Indexed: 11/29/2022]
Abstract
We compare the information transmission of a time-dependent signal by two types of uncoupled neuron populations that differ in their sources of variability: i) a homogeneous population whose units receive independent noise and ii) a deterministic heterogeneous population, where each unit exhibits a different baseline firing rate ('disorder'). Our criterion for making both sources of variability quantitatively comparable is that the interspike-interval distributions are identical for both systems. Numerical simulations using leaky integrate-and-fire neurons unveil that a non-zero amount of both noise or disorder maximizes the encoding efficiency of the homogeneous and heterogeneous system, respectively, as a particular case of suprathreshold stochastic resonance. Our findings thus illustrate that heterogeneity can render similarly profitable effects for neuronal populations as dynamic noise. The optimal noise/disorder depends on the system size and the properties of the stimulus such as its intensity or cutoff frequency. We find that weak stimuli are better encoded by a noiseless heterogeneous population, whereas for strong stimuli a homogeneous population outperforms an equivalent heterogeneous system up to a moderate noise level. Furthermore, we derive analytical expressions of the coherence function for the cases of very strong noise and of vanishing intrinsic noise or heterogeneity, which predict the existence of an optimal noise intensity. Our results show that, depending on the type of signal, noise as well as heterogeneity can enhance the encoding performance of neuronal populations.
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Affiliation(s)
- Manuel Beiran
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany. .,Group for Neural Theory, Laboratoire de Neurosciences Cognitives, Département Études Cognitives, École Normale Supérieure, INSERM, PSL Research University, Paris, France.
| | - Alexandra Kruscha
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Physics Department, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jan Benda
- Institute for Neurobiology, Eberhard Karls Universität, Tübingen, Germany
| | - Benjamin Lindner
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Physics Department, Humboldt-Universität zu Berlin, Berlin, Germany
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50
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Ly C, Marsat G. Variable synaptic strengths controls the firing rate distribution in feedforward neural networks. J Comput Neurosci 2017; 44:75-95. [DOI: 10.1007/s10827-017-0670-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 12/27/2022]
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