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Kay K, Prince JS, Gebhart T, Tuckute G, Zhou J, Naselaris T, Schutt H. Disentangling signal and noise in neural responses through generative modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590510. [PMID: 38712051 PMCID: PMC11071385 DOI: 10.1101/2024.04.22.590510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal , operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we describe a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method-termed Generative Modeling of Signal and Noise (GSN)-the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. Importantly, GSN improves estimates of the signal distribution, but does not provide improved estimates of responses to individual events. We validate GSN using ground-truth simulations and show that it compares favorably with related methods. We also demonstrate the application of GSN to empirical fMRI data to illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSN's characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.
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2
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Xia J, Jasper A, Kohn A, Miller KD. Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability. iScience 2024; 27:110512. [PMID: 39156642 PMCID: PMC11328009 DOI: 10.1016/j.isci.2024.110512] [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: 12/19/2023] [Revised: 04/01/2024] [Accepted: 07/12/2024] [Indexed: 08/20/2024] Open
Abstract
Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a multiplicative factor and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models and provide a parsimonious explanation for the stimulus dependence of correlated variability within and between visual areas.
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Affiliation(s)
- Ji Xia
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Anna Jasper
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenneth D. Miller
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027, USA
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3
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Tang D, Zylberberg J, Jia X, Choi H. Stimulus type shapes the topology of cellular functional networks in mouse visual cortex. Nat Commun 2024; 15:5753. [PMID: 38982078 PMCID: PMC11233648 DOI: 10.1038/s41467-024-49704-0] [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: 07/03/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
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Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University, Toronto, ON M3J 1P3, ON, Canada.
- Learning in Machines and Brains Program, CIFAR, Toronto, ON M5G 1M1, ON, Canada.
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, PR China.
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- School of Mathematics, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
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4
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Chini M, Hnida M, Kostka JK, Chen YN, Hanganu-Opatz IL. Preconfigured architecture of the developing mouse brain. Cell Rep 2024; 43:114267. [PMID: 38795344 DOI: 10.1016/j.celrep.2024.114267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/13/2024] [Accepted: 05/08/2024] [Indexed: 05/27/2024] Open
Abstract
In the adult brain, structural and functional parameters, such as synaptic sizes and neuronal firing rates, follow right-skewed and heavy-tailed distributions. While this organization is thought to have significant implications, its development is still largely unknown. Here, we address this knowledge gap by investigating a large-scale dataset recorded from the prefrontal cortex and the olfactory bulb of mice aged 4-60 postnatal days. We show that firing rates and spike train interactions have a largely stable distribution shape throughout the first 60 postnatal days and that the prefrontal cortex displays a functional small-world architecture. Moreover, early brain activity exhibits an oligarchical organization, where high-firing neurons have hub-like properties. In a neural network model, we show that analogously right-skewed and heavy-tailed synaptic parameters are instrumental to consistently recapitulate the experimental data. Thus, functional and structural parameters in the developing brain are already extremely distributed, suggesting that this organization is preconfigured and not experience dependent.
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Affiliation(s)
- Mattia Chini
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Marilena Hnida
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Johanna K Kostka
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yu-Nan Chen
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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5
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Zhou J, Duong LR, Simoncelli EP. A unified framework for perceived magnitude and discriminability of sensory stimuli. Proc Natl Acad Sci U S A 2024; 121:e2312293121. [PMID: 38857385 PMCID: PMC11194506 DOI: 10.1073/pnas.2312293121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 04/25/2024] [Indexed: 06/12/2024] Open
Abstract
The perception of sensory attributes is often quantified through measurements of sensitivity (the ability to detect small stimulus changes), as well as through direct judgments of appearance or intensity. Despite their ubiquity, the relationship between these two measurements remains controversial and unresolved. Here, we propose a framework in which they arise from different aspects of a common representation. Specifically, we assume that judgments of stimulus intensity (e.g., as measured through rating scales) reflect the mean value of an internal representation, and sensitivity reflects a combination of mean value and noise properties, as quantified by the statistical measure of Fisher information. Unique identification of these internal representation properties can be achieved by combining measurements of sensitivity and judgments of intensity. As a central example, we show that Weber's law of perceptual sensitivity can coexist with Stevens' power-law scaling of intensity ratings (for all exponents), when the noise amplitude increases in proportion to the representational mean. We then extend this result beyond the Weber's law range by incorporating a more general and physiology-inspired form of noise and show that the combination of noise properties and sensitivity measurements accurately predicts intensity ratings across a variety of sensory modalities and attributes. Our framework unifies two primary perceptual measurements-thresholds for sensitivity and rating scales for intensity-and provides a neural interpretation for the underlying representation.
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Affiliation(s)
- Jingyang Zhou
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, NY10010
- Center for Neural Science, New York University, New York, NY10003
| | - Lyndon R. Duong
- Center for Neural Science, New York University, New York, NY10003
| | - Eero P. Simoncelli
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, NY10010
- Center for Neural Science, New York University, New York, NY10003
- Courant Institute of Mathematical Sciences, New York University, New York, NY10003
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6
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Wei 魏赣超 G, Tajik Mansouri زینب تاجیک منصوری Z, Wang 王晓婧 X, Stevenson IH. Calibrating Bayesian Decoders of Neural Spiking Activity. J Neurosci 2024; 44:e2158232024. [PMID: 38538143 PMCID: PMC11063820 DOI: 10.1523/jneurosci.2158-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 03/11/2024] [Indexed: 05/03/2024] Open
Abstract
Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.
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Affiliation(s)
- Ganchao Wei 魏赣超
- Department of Statistical Science, Duke University, Durham, North Carolina 27708
| | | | | | - Ian H Stevenson
- Departments of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269
- Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Connecticut Institute for Brain and Cognitive Science, University of Connecticut, Storrs, Connecticut 06269
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7
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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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8
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de Brito CSN, Gerstner W. Learning what matters: Synaptic plasticity with invariance to second-order input correlations. PLoS Comput Biol 2024; 20:e1011844. [PMID: 38346073 PMCID: PMC10890752 DOI: 10.1371/journal.pcbi.1011844] [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: 12/01/2022] [Revised: 02/23/2024] [Accepted: 01/18/2024] [Indexed: 02/25/2024] Open
Abstract
Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, our learning objective explains the functional form of observed excitatory plasticity mechanisms, showing how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations so that receptive fields become aligned with features hidden in higher-order statistics. Invariance to second-order correlations enhances the versatility of biologically realistic learning models, supporting optimal decoding from noisy inputs and sparse population coding from spatially correlated stimuli. In a spiking model with triplet spike-timing-dependent plasticity (STDP), we show that individual neurons can learn localized oriented receptive fields, circumventing the need for input preprocessing, such as whitening, or population-level lateral inhibition. The theory advances our understanding of local unsupervised learning in cortical circuits, offers new interpretations of the Bienenstock-Cooper-Munro and triplet STDP models, and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.
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Affiliation(s)
- Carlos Stein Naves de Brito
- École Polytechnique Fédérale de Lausanne, EPFL, Lusanne, Switzerland
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Wulfram Gerstner
- École Polytechnique Fédérale de Lausanne, EPFL, Lusanne, Switzerland
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9
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Bhaskaran AA, Gauvrit T, Vyas Y, Bony G, Ginger M, Frick A. Endogenous noise of neocortical neurons correlates with atypical sensory response variability in the Fmr1 -/y mouse model of autism. Nat Commun 2023; 14:7905. [PMID: 38036566 PMCID: PMC10689491 DOI: 10.1038/s41467-023-43777-z] [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: 02/10/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023] Open
Abstract
Excessive neural variability of sensory responses is a hallmark of atypical sensory processing in autistic individuals with cascading effects on other core autism symptoms but unknown neurobiological substrate. Here, by recording neocortical single neuron activity in a well-established mouse model of Fragile X syndrome and autism, we characterized atypical sensory processing and probed the role of endogenous noise sources in exaggerated response variability in males. The analysis of sensory stimulus evoked activity and spontaneous dynamics, as well as neuronal features, reveals a complex cellular and network phenotype. Neocortical sensory information processing is more variable and temporally imprecise. Increased trial-by-trial and inter-neuronal response variability is strongly related to key endogenous noise features, and may give rise to behavioural sensory responsiveness variability in autism. We provide a novel preclinical framework for understanding the sources of endogenous noise and its contribution to core autism symptoms, and for testing the functional consequences for mechanism-based manipulation of noise.
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Affiliation(s)
- Arjun A Bhaskaran
- INSERM, U1215 Neurocentre Magendie, 33077, Bordeaux, France
- University of Bordeaux, 33000, Bordeaux, France
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Théo Gauvrit
- INSERM, U1215 Neurocentre Magendie, 33077, Bordeaux, France
- University of Bordeaux, 33000, Bordeaux, France
| | - Yukti Vyas
- INSERM, U1215 Neurocentre Magendie, 33077, Bordeaux, France
- University of Bordeaux, 33000, Bordeaux, France
| | - Guillaume Bony
- INSERM, U1215 Neurocentre Magendie, 33077, Bordeaux, France
- University of Bordeaux, 33000, Bordeaux, France
| | - Melanie Ginger
- INSERM, U1215 Neurocentre Magendie, 33077, Bordeaux, France
- University of Bordeaux, 33000, Bordeaux, France
| | - Andreas Frick
- INSERM, U1215 Neurocentre Magendie, 33077, Bordeaux, France.
- University of Bordeaux, 33000, Bordeaux, France.
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10
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Cowley BR, Stan PL, Pillow JW, Smith MA. Compact deep neural network models of visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.22.568315. [PMID: 38045255 PMCID: PMC10690296 DOI: 10.1101/2023.11.22.568315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
A powerful approach to understanding the computations carried out in visual cortex is to develop models that predict neural responses to arbitrary images. Deep neural network (DNN) models have worked remarkably well at predicting neural responses [1, 2, 3], yet their underlying computations remain buried in millions of parameters. Have we simply replaced one complicated system in vivo with another in silico ? Here, we train a data-driven deep ensemble model that predicts macaque V4 responses ∼50% more accurately than currently-used task-driven DNN models. We then compress this deep ensemble to identify compact models that have 5,000x fewer parameters yet equivalent accuracy as the deep ensemble. We verified that the stimulus preferences of the compact models matched those of the real V4 neurons by measuring V4 responses to both 'maximizing' and adversarial images generated using compact models. We then analyzed the inner workings of the compact models and discovered a common circuit motif: Compact models share a similar set of filters in early stages of processing but then specialize by heavily consolidating this shared representation with a precise readout. This suggests that a V4 neuron's stimulus preference is determined entirely by its consolidation step. To demonstrate this, we investigated the compression step of a dot-detecting compact model and found a set of simple computations that may be carried out by dot-selective V4 neurons. Overall, our work demonstrates that the DNN models currently used in computational neuroscience are needlessly large; our approach provides a new way forward for obtaining explainable, high-accuracy models of visual cortical neurons.
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11
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Weiss O, Bounds HA, Adesnik H, Coen-Cagli R. Modeling the diverse effects of divisive normalization on noise correlations. PLoS Comput Biol 2023; 19:e1011667. [PMID: 38033166 PMCID: PMC10715670 DOI: 10.1371/journal.pcbi.1011667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/12/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.
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Affiliation(s)
- Oren Weiss
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Hayley A. Bounds
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, United States of America
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12
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Ayar EC, Heusser MR, Bourrelly C, Gandhi NJ. Distinct context- and content-dependent population codes in superior colliculus during sensation and action. Proc Natl Acad Sci U S A 2023; 120:e2303523120. [PMID: 37748075 PMCID: PMC10556644 DOI: 10.1073/pnas.2303523120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 08/23/2023] [Indexed: 09/27/2023] Open
Abstract
Sensorimotor transformation is the process of first sensing an object in the environment and then producing a movement in response to that stimulus. For visually guided saccades, neurons in the superior colliculus (SC) emit a burst of spikes to register the appearance of stimulus, and many of the same neurons discharge another burst to initiate the eye movement. We investigated whether the neural signatures of sensation and action in SC depend on context. Spiking activity along the dorsoventral axis was recorded with a laminar probe as Rhesus monkeys generated saccades to the same stimulus location in tasks that require either executive control to delay saccade onset until permission is granted or the production of an immediate response to a target whose onset is predictable. Using dimensionality reduction and discriminability methods, we show that the subspaces occupied during the visual and motor epochs were both distinct within each task and differentiable across tasks. Single-unit analyses, in contrast, show that the movement-related activity of SC neurons was not different between tasks. These results demonstrate that statistical features in neural activity of simultaneously recorded ensembles provide more insight than single neurons. They also indicate that cognitive processes associated with task requirements are multiplexed in SC population activity during both sensation and action and that downstream structures could use this activity to extract context. Additionally, the entire manifolds associated with sensory and motor responses, respectively, may be larger than the subspaces explored within a certain set of experiments.
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Affiliation(s)
- Eve C. Ayar
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA15213
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA15213
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA15213
| | - Michelle R. Heusser
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA15213
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA15213
| | - Clara Bourrelly
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA15213
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA15213
| | - Neeraj J. Gandhi
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA15213
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA15213
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA15213
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA15213
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA15213
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13
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Glanz RM, Sokoloff G, Blumberg MS. Neural decoding reveals specialized kinematic tuning after an abrupt cortical transition. Cell Rep 2023; 42:113119. [PMID: 37690023 PMCID: PMC10591925 DOI: 10.1016/j.celrep.2023.113119] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/08/2023] [Accepted: 08/24/2023] [Indexed: 09/12/2023] Open
Abstract
The primary motor cortex (M1) exhibits a protracted period of development, including the development of a sensory representation long before motor outflow emerges. In rats, this representation is present by postnatal day (P) 8, when M1 activity is "discontinuous." Here, we ask how the representation changes upon the transition to "continuous" activity at P12. We use neural decoding to predict forelimb movements from M1 activity and show that a linear decoder effectively predicts limb movements at P8 but not at P12; instead, a nonlinear decoder better predicts limb movements at P12. The altered decoder performance reflects increased complexity and uniqueness of kinematic information in M1. We next show that M1's representation at P12 is more susceptible to "lesioning" of inputs and "transplanting" of M1's encoding scheme from one pup to another. Thus, the emergence of continuous M1 activity signals the developmental onset of more complex, informationally sparse, and individualized sensory representations.
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Affiliation(s)
- Ryan M Glanz
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - Greta Sokoloff
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA
| | - Mark S Blumberg
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA.
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14
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Levenstein D, Okun M. Logarithmically scaled, gamma distributed neuronal spiking. J Physiol 2023; 601:3055-3069. [PMID: 36086892 PMCID: PMC10952267 DOI: 10.1113/jp282758] [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: 05/09/2022] [Accepted: 07/28/2022] [Indexed: 11/08/2022] Open
Abstract
Naturally log-scaled quantities abound in the nervous system. Distributions of these quantities have non-intuitive properties, which have implications for data analysis and the understanding of neural circuits. Here, we review the log-scaled statistics of neuronal spiking and the relevant analytical probability distributions. Recent work using log-scaling revealed that interspike intervals of forebrain neurons segregate into discrete modes reflecting spiking at different timescales and are each well-approximated by a gamma distribution. Each neuron spends most of the time in an irregular spiking 'ground state' with the longest intervals, which determines the mean firing rate of the neuron. Across the entire neuronal population, firing rates are log-scaled and well approximated by the gamma distribution, with a small number of highly active neurons and an overabundance of low rate neurons (the 'dark matter'). These results are intricately linked to a heterogeneous balanced operating regime, which confers upon neuronal circuits multiple computational advantages and has evolutionarily ancient origins.
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Affiliation(s)
- Daniel Levenstein
- Department of Neurology and NeurosurgeryMcGill UniversityMontrealQCCanada
- MilaMontréalQCCanada
| | - Michael Okun
- Department of Psychology and Neuroscience InstituteUniversity of SheffieldSheffieldUK
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15
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Mahuas G, Marre O, Mora T, Ferrari U. Small-correlation expansion to quantify information in noisy sensory systems. Phys Rev E 2023; 108:024406. [PMID: 37723816 DOI: 10.1103/physreve.108.024406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 06/26/2023] [Indexed: 09/20/2023]
Abstract
Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with a complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations. Here, we develop a small-correlation expansion to compute the stimulus information carried by a large population of neurons, yielding interpretable analytical expressions in terms of the neurons' firing rates and pairwise correlations. We validate the approximation on synthetic data and demonstrate its applicability to electrophysiological recordings in the vertebrate retina, allowing us to quantify the effects of noise correlations between neurons and of memory in single neurons.
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Affiliation(s)
- Gabriel Mahuas
- Institut de la Vision, Sorbonne Université, CNRS, INSERM, 17 rue Moreau, 75012 Paris, France
- Laboratoire de Physique de École Normale Supérieure, CNRS, PSL University, Sorbonne University, Université Paris-Cité, 24 rue Lhomond, 75005 Paris, France
| | - Olivier Marre
- Institut de la Vision, Sorbonne Université, CNRS, INSERM, 17 rue Moreau, 75012 Paris, France
| | - Thierry Mora
- Laboratoire de Physique de École Normale Supérieure, CNRS, PSL University, Sorbonne University, Université Paris-Cité, 24 rue Lhomond, 75005 Paris, France
| | - Ulisse Ferrari
- Institut de la Vision, Sorbonne Université, CNRS, INSERM, 17 rue Moreau, 75012 Paris, France
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16
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Zhu RJB, Wei XX. Unsupervised approach to decomposing neural tuning variability. Nat Commun 2023; 14:2298. [PMID: 37085524 PMCID: PMC10121715 DOI: 10.1038/s41467-023-37982-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/07/2023] [Indexed: 04/23/2023] Open
Abstract
Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture the moment-to-moment tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex- a paradigmatic case for which the tuning curve approach has been scientifically essential- we discovered a simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of unexpected structure of the neural code, capturing the influence of the external stimulus drive and internal states simultaneously.
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Affiliation(s)
- Rong J B Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Shanghai, China.
| | - Xue-Xin Wei
- Department of Neuroscience, The University of Texas at Austin, Austin, USA.
- Department of Psychology, The University of Texas at Austin, Austin, USA.
- Center for Perceptual Systems, The University of Texas at Austin, Austin, USA.
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, USA.
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17
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Boucher-Routhier M, Thivierge JP. A deep generative adversarial network capturing complex spiral waves in disinhibited circuits of the cerebral cortex. BMC Neurosci 2023; 24:22. [PMID: 36964493 PMCID: PMC10039524 DOI: 10.1186/s12868-023-00792-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/17/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND In the cerebral cortex, disinhibited activity is characterized by propagating waves that spread across neural tissue. In this pathological state, a widely reported form of activity are spiral waves that travel in a circular pattern around a fixed spatial locus termed the center of mass. Spiral waves exhibit stereotypical activity and involve broad patterns of co-fluctuations, suggesting that they may be of lower complexity than healthy activity. RESULTS To evaluate this hypothesis, we performed dense multi-electrode recordings of cortical networks where disinhibition was induced by perfusing a pro-epileptiform solution containing 4-Aminopyridine as well as increased potassium and decreased magnesium. Spiral waves were identified based on a spatially delimited center of mass and a broad distribution of instantaneous phases across electrodes. Individual waves were decomposed into "snapshots" that captured instantaneous neural activation across the entire network. The complexity of these snapshots was examined using a measure termed the participation ratio. Contrary to our expectations, an eigenspectrum analysis of these snapshots revealed a broad distribution of eigenvalues and an increase in complexity compared to baseline networks. A deep generative adversarial network was trained to generate novel exemplars of snapshots that closely captured cortical spiral waves. These synthetic waves replicated key features of experimental data including a tight center of mass, a broad eigenvalue distribution, spatially-dependent correlations, and a high complexity. By adjusting the input to the model, new samples were generated that deviated in systematic ways from the experimental data, thus allowing the exploration of a broad range of states from healthy to pathologically disinhibited neural networks. CONCLUSIONS Together, results show that the complexity of population activity serves as a marker along a continuum from healthy to disinhibited brain states. The proposed generative adversarial network opens avenues for replicating the dynamics of cortical seizures and accelerating the design of optimal neurostimulation aimed at suppressing pathological brain activity.
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Affiliation(s)
- Megan Boucher-Routhier
- School of Psychology, University of Ottawa, 156 Jean-Jacques Lussier, Ottawa, ON, K1N 6N5, Canada
| | - Jean-Philippe Thivierge
- School of Psychology, University of Ottawa, 156 Jean-Jacques Lussier, Ottawa, ON, K1N 6N5, Canada.
- University of Ottawa Brain and Mind Research Institute, 451 Smyth Rd., Ottawa, ON, K1H 8M5, Canada.
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18
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Haggard M, Chacron MJ. Coding of object location by heterogeneous neural populations with spatially dependent correlations in weakly electric fish. PLoS Comput Biol 2023; 19:e1010938. [PMID: 36867650 PMCID: PMC10016687 DOI: 10.1371/journal.pcbi.1010938] [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: 10/12/2022] [Revised: 03/15/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Understanding how neural populations encode sensory stimuli remains a central problem in neuroscience. Here we performed multi-unit recordings from sensory neural populations in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus in response to stimuli located at different positions along the rostro-caudal axis. Our results reveal that the spatial dependence of correlated activity along receptive fields can help mitigate the deleterious effects that these correlations would otherwise have if they were spatially independent. Moreover, using mathematical modeling, we show that experimentally observed heterogeneities in the receptive fields of neurons help optimize information transmission as to object location. Taken together, our results have important implications for understanding how sensory neurons whose receptive fields display antagonistic center-surround organization encode location. Important similarities between the electrosensory system and other sensory systems suggest that our results will be applicable elsewhere.
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Affiliation(s)
- Myriah Haggard
- Quantitative Life Sciences, McGill University, Montreal, Canada
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19
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Glanz R, Sokoloff G, Blumberg MS. Cortical Representation of Movement Across the Developmental Transition to Continuous Neural Activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.22.525085. [PMID: 36711887 PMCID: PMC9882351 DOI: 10.1101/2023.01.22.525085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Primary motor cortex (M1) exhibits a protracted period of development that includes the establishment of a somatosensory map long before motor outflow emerges. In rats, the sensory representation is established by postnatal day (P) 8 when cortical activity is still "discontinuous." Here, we ask how the representation survives the sudden transition to noisy "continuous" activity at P12. Using neural decoding to predict forelimb movements based solely on M1 activity, we show that a linear decoder is sufficient to predict limb movements at P8, but not at P12; in contrast, a nonlinear decoder effectively predicts limb movements at P12. The change in decoder performance at P12 reflects an increase in both the complexity and uniqueness of kinematic information available in M1. We next show that the representation at P12 is more susceptible to the deleterious effects of "lesioning" inputs and to "transplanting" M1's encoding scheme from one pup to another. We conclude that the emergence of continuous cortical activity signals the developmental onset in M1 of more complex, informationally sparse, and individualized sensory representations.
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Affiliation(s)
- Ryan Glanz
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242 USA
| | - Greta Sokoloff
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242 USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242 USA
| | - Mark S. Blumberg
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242 USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242 USA
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20
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Mosheiff N, Ermentrout B, Huang C. Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability. PLoS Comput Biol 2023; 19:e1010843. [PMID: 36626362 PMCID: PMC9870129 DOI: 10.1371/journal.pcbi.1010843] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 01/23/2023] [Accepted: 12/26/2022] [Indexed: 01/11/2023] Open
Abstract
Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.
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Affiliation(s)
- Noga Mosheiff
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
| | - Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Chengcheng Huang
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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21
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Efficient coding theory of dynamic attentional modulation. PLoS Biol 2022; 20:e3001889. [PMID: 36542662 PMCID: PMC9831638 DOI: 10.1371/journal.pbio.3001889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/10/2023] [Accepted: 10/24/2022] [Indexed: 12/24/2022] Open
Abstract
Activity of sensory neurons is driven not only by external stimuli but also by feedback signals from higher brain areas. Attention is one particularly important internal signal whose presumed role is to modulate sensory representations such that they only encode information currently relevant to the organism at minimal cost. This hypothesis has, however, not yet been expressed in a normative computational framework. Here, by building on normative principles of probabilistic inference and efficient coding, we developed a model of dynamic population coding in the visual cortex. By continuously adapting the sensory code to changing demands of the perceptual observer, an attention-like modulation emerges. This modulation can dramatically reduce the amount of neural activity without deteriorating the accuracy of task-specific inferences. Our results suggest that a range of seemingly disparate cortical phenomena such as intrinsic gain modulation, attention-related tuning modulation, and response variability could be manifestations of the same underlying principles, which combine efficient sensory coding with optimal probabilistic inference in dynamic environments.
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22
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Ash RT, Palagina G, Fernandez-Leon JA, Park J, Seilheimer R, Lee S, Sabharwal J, Reyes F, Wang J, Lu D, Sarfraz M, Froudarakis E, Tolias AS, Wu SM, Smirnakis SM. Increased Reliability of Visually-Evoked Activity in Area V1 of the MECP2-Duplication Mouse Model of Autism. J Neurosci 2022; 42:6469-6482. [PMID: 35831173 PMCID: PMC9398540 DOI: 10.1523/jneurosci.0654-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/15/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in layer 2/3 neurons of adult male and female primary visual cortex in the MECP2-duplication syndrome animal model of autism. Increased response reliability was due in part to decreased response amplitude, decreased fluctuations in endogenous activity, and an abnormal decoupling of visual-evoked activity from endogenous activity. Similar to what was observed neuronally, the optokinetic reflex occurred more reliably at low contrasts in mutant mice compared with controls. Retinal responses did not explain our observations. These data suggest that the circuit mechanisms for combining sensory-evoked and endogenous signal and noise processes may be altered in this form of syndromic autism.SIGNIFICANCE STATEMENT Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in primary visual cortex of the animal model for MECP2-duplication syndrome, a high-penetrance single-gene cause of autism. Visual-evoked activity was abnormally decoupled from endogenous activity in mutant mice, suggesting in line with the influential "hypo-priors" theory of autism that sensory priors embedded in endogenous activity may have less influence on perception in autism.
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Affiliation(s)
- Ryan T Ash
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Ganna Palagina
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Jose A Fernandez-Leon
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires and Instituto de Investigación en Tecnología Informática Avanzada, Exact Sciences Faculty-Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina
| | - Jiyoung Park
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas 77030
| | - Rob Seilheimer
- Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas 77030
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Sangkyun Lee
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Jasdeep Sabharwal
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland 21205
| | - Fredy Reyes
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Jing Wang
- Department of Ophthalmology, Baylor College of Medicine, Houston, Texas 77030
| | - Dylan Lu
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Muhammad Sarfraz
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- FORTH, Heraklion, Crete, Greece 70013
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Samuel M Wu
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Department of Ophthalmology, Baylor College of Medicine, Houston, Texas 77030
| | - Stelios M Smirnakis
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
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23
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Li Y, Wang T, Yang Y, Dai W, Wu Y, Li L, Han C, Zhong L, Li L, Wang G, Dou F, Xing D. Cascaded normalizations for spatial integration in the primary visual cortex of primates. Cell Rep 2022; 40:111221. [PMID: 35977486 DOI: 10.1016/j.celrep.2022.111221] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 04/19/2022] [Accepted: 07/25/2022] [Indexed: 11/03/2022] Open
Abstract
Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.
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Affiliation(s)
- Yang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; College of Life Sciences, Beijing Normal University, Beijing 100875, China
| | - Yi Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Weifeng Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yujie Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lianfeng Li
- China Academy of Launch Vehicle Technology, Beijing 100076, China
| | - Chuanliang Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lvyan Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Liang Li
- Beijing Institute of Basic Medical Sciences, Beijing 100005, China
| | - Gang Wang
- Beijing Institute of Basic Medical Sciences, Beijing 100005, China
| | - Fei Dou
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; College of Life Sciences, Beijing Normal University, Beijing 100875, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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24
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Bhasin G, Nair IR. Dynamic Hippocampal CA2 Responses to Contextual Spatial Novelty. Front Syst Neurosci 2022; 16:923911. [PMID: 36003545 PMCID: PMC9393711 DOI: 10.3389/fnsys.2022.923911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Hippocampal place cells are functional units of spatial navigation and are present in all subregions: CA1, CA2, CA3, and CA4. Recent studies on CA2 have indicated its role in social and contextual memories, but its contribution to spatial novelty detection and encoding remains largely unknown. The current study aims to uncover how CA2 processes spatial novelty and to distinguish its functional role towards the same from CA1. Accordingly, a novel 3-day paradigm was designed where animals were introduced to a completely new environment on the first day, and on subsequent days, novel segments were inserted into the existing spatial environment while the other segments remained the same, allowing us to compare novel and familiar parts of the same closed-loop track on multiple days. We found that spatial novelty leads to dynamic and complex hippocampal place cell firings at both individual neuron and population levels. Place cells in both CA1 and CA2 had strong responses to novel segments, leading to higher average firing rates and increased pairwise cross correlations across all days. However, CA2 place cells that fired for novel areas had lower spatial information scores than CA1 place cells active in the same areas. At the ensemble level, CA1 only responded to spatial novelty on day 1, when the environment was completely novel, whereas CA2 responded to it on all days, each time novelty was introduced. Therefore, CA2 was more sensitive and responsive to novel spatial features even when introduced in a familiar environment, unlike CA1.
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25
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Hao X, Liu Q, Chan J, Li N, Shi X, Gu Y. Dark exposure can partly restore the disrupted cortical reliability Binocular visual experience drives the maturation of response variability and reliability in the visual cortex. iScience 2022; 25:104984. [PMID: 36105593 PMCID: PMC9465340 DOI: 10.1016/j.isci.2022.104984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/23/2022] [Accepted: 08/16/2022] [Indexed: 10/25/2022] Open
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26
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Ni AM, Huang C, Doiron B, Cohen MR. A general decoding strategy explains the relationship between behavior and correlated variability. eLife 2022; 11:67258. [PMID: 35660134 PMCID: PMC9170243 DOI: 10.7554/elife.67258] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/11/2022] [Indexed: 11/16/2022] Open
Abstract
Improvements in perception are frequently accompanied by decreases in correlated variability in sensory cortex. This relationship is puzzling because overall changes in correlated variability should minimally affect optimal information coding. We hypothesize that this relationship arises because instead of using optimal strategies for decoding the specific stimuli at hand, observers prioritize generality: a single set of neuronal weights to decode any stimuli. We tested this using a combination of multineuron recordings in the visual cortex of behaving rhesus monkeys and a cortical circuit model. We found that general decoders optimized for broad rather than narrow sets of visual stimuli better matched the animals’ decoding strategy, and that their performance was more related to the magnitude of correlated variability. In conclusion, the inverse relationship between perceptual performance and correlated variability can be explained by observers using a general decoding strategy, capable of decoding neuronal responses to the variety of stimuli encountered in natural vision.
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Affiliation(s)
- Amy M Ni
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States
| | - Chengcheng Huang
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States.,Department of Mathematics, University of Pittsburgh, Pittsburgh, United States
| | - Brent Doiron
- Center for the Neural Basis of Cognition, Pittsburgh, United States.,Department of Mathematics, University of Pittsburgh, Pittsburgh, United States
| | - Marlene R Cohen
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States
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27
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Huang C, Pouget A, Doiron B. Internally generated population activity in cortical networks hinders information transmission. SCIENCE ADVANCES 2022; 8:eabg5244. [PMID: 35648863 PMCID: PMC9159697 DOI: 10.1126/sciadv.abg5244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/14/2022] [Indexed: 06/15/2023]
Abstract
How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry establishes neural codes. We study stimulus coding in networks of spiking neuron models with spatially ordered excitatory and inhibitory connectivity. The wiring structure is capable of producing rich population-wide shared neuronal variability that agrees with many features of recorded cortical activity. While both the spatial scales of feedforward and recurrent projections strongly affect noise correlations, only recurrent projections, and in particular inhibitory projections, can introduce correlations that limit the stimulus information available to a decoder. Using a spatial neural field model, we relate the recurrent circuit conditions for information limiting noise correlations to how recurrent excitation and inhibition can form spatiotemporal patterns of population-wide activity.
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Affiliation(s)
- Chengcheng Huang
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Brent Doiron
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Departments of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
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28
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Baratham VL, Dougherty ME, Hermiz J, Ledochowitsch P, Maharbiz MM, Bouchard KE. Columnar Localization and Laminar Origin of Cortical Surface Electrical Potentials. J Neurosci 2022; 42:3733-3748. [PMID: 35332084 PMCID: PMC9087723 DOI: 10.1523/jneurosci.1787-21.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 02/09/2022] [Accepted: 03/09/2022] [Indexed: 11/21/2022] Open
Abstract
Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized μECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, μECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to less than or equal to ±200 μm, that is, a single cortical column. Biophysical simulations reproduce experimental findings and indicate that neurons in cortical layers V and VI contribute ∼85% of evoked high-gamma signal recorded at the surface. Cell number and synchrony were the primary biophysical properties determining laminar contributions to evoked μECoG signals, whereas distance was only a minimal factor. Thus, evoked μECoG signals primarily originate from neurons in the infragranular layers of a single cortical column.SIGNIFICANCE STATEMENT ECoG methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. We investigated the localization and origins of sensory-evoked ECoG responses. We experimentally found that ECoG responses were anisotropically localized to a cortical column. Biophysically detailed simulations revealed that neurons in layers V and VI were the primary sources of evoked ECoG responses. These results indicate that evoked ECoG high-gamma responses are primarily generated by the population spike rate of pyramidal neurons in layers V and VI of single cortical columns and highlight the possibility of understanding how microscopic sources produce mesoscale signals.
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Affiliation(s)
- Vyassa L Baratham
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California-Berkeley, Berkeley, California 94720
| | - Maximilian E Dougherty
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - John Hermiz
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - Michel M Maharbiz
- Center for Neural Engineering and Prosthesis, University of California-Berkeley/San Francisco, Berkeley, California 94720-3370
- Department of Electrical Engineering and Computer Science, University of California-Berkeley, Berkeley, California 94720
| | - Kristofer E Bouchard
- Center for Neural Engineering and Prosthesis, University of California-Berkeley/San Francisco, Berkeley, California 94720-3370
- Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California-Berkeley, Berkeley, California 94720
- Scientific Data Division, Lawerence Berkeley National Lab, Berkeley, California 94720
- Biological Systems and Engineering Division, Lawerence Berkeley National Lab, Berkeley, California 94720
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29
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Sheng J, Zhang L, Liu C, Liu J, Feng J, Zhou Y, Hu H, Xue G. Higher-dimensional neural representations predict better episodic memory. SCIENCE ADVANCES 2022; 8:eabm3829. [PMID: 35442734 PMCID: PMC9020666 DOI: 10.1126/sciadv.abm3829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
Episodic memory enables humans to encode and later vividly retrieve information about our rich experiences, yet the neural representations that support this mental capacity are poorly understood. Using a large fMRI dataset (n = 468) of face-name associative memory tasks and principal component analysis to examine neural representational dimensionality (RD), we found that the human brain maintained a high-dimensional representation of faces through hierarchical representation within and beyond the face-selective regions. Critically, greater RD was associated with better subsequent memory performance both within and across participants, and this association was specific to episodic memory but not general cognitive abilities. Furthermore, the frontoparietal activities could suppress the shared low-dimensional fluctuations and reduce the correlations of local neural responses, resulting in greater RD. RD was not associated with the degree of item-specific pattern similarity, and it made complementary contributions to episodic memory. These results provide a mechanistic understanding of the role of RD in supporting accurate episodic memory.
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30
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Triplett MA, Goodhill GJ. Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data. Neural Comput 2022; 34:1143-1169. [PMID: 35344990 DOI: 10.1162/neco_a_01492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/11/2022] [Indexed: 11/04/2022]
Abstract
Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor-analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.
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Affiliation(s)
- Marcus A Triplett
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD 4072, Australia
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31
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Joshi S, Gold JI. Context-dependent relationships between locus coeruleus firing patterns and coordinated neural activity in the anterior cingulate cortex. eLife 2022; 11:63490. [PMID: 34994344 PMCID: PMC8765756 DOI: 10.7554/elife.63490] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/16/2021] [Indexed: 01/30/2023] Open
Abstract
Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these neuromodulatory effects on neural activity patterns in vivo is not well understood. Here, we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These relationships, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations tend to be reduced when ongoing (baseline) LC activity increases but enhanced when external events evoke transient LC responses. Both relationships covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can result partly from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.
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Affiliation(s)
- Siddhartha Joshi
- Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
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32
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Abstract
A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.
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33
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Huang C. Modulation of the dynamical state in cortical network models. Curr Opin Neurobiol 2021; 70:43-50. [PMID: 34403890 PMCID: PMC8688204 DOI: 10.1016/j.conb.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/18/2021] [Accepted: 07/14/2021] [Indexed: 11/29/2022]
Abstract
Cortical neural responses can be modulated by various factors, such as stimulus inputs and the behavior state of the animal. Understanding the circuit mechanisms underlying modulations of network dynamics is important to understand the flexibility of circuit computations. Identifying the dynamical state of a network is an important first step to predict network responses to external stimulus and top-down modulatory inputs. Models in stable or unstable dynamical regimes require different analytic tools to estimate the network responses to inputs and the structure of neural variability. In this article, I review recent cortical models of state-dependent responses and their predictions about the underlying modulatory mechanisms.
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Affiliation(s)
- Chengcheng Huang
- Departments of Neuroscience and Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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34
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Umakantha A, Morina R, Cowley BR, Snyder AC, Smith MA, Yu BM. Bridging neuronal correlations and dimensionality reduction. Neuron 2021; 109:2740-2754.e12. [PMID: 34293295 PMCID: PMC8505167 DOI: 10.1016/j.neuron.2021.06.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 05/05/2021] [Accepted: 06/25/2021] [Indexed: 01/01/2023]
Abstract
Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.
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Affiliation(s)
- Akash Umakantha
- Carnegie Mellon Neuroscience Institute, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Rudina Morina
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Benjamin R Cowley
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Adam C Snyder
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA; Department of Neuroscience, University of Rochester, Rochester, NY 14642, USA; Center for Visual Science, University of Rochester, Rochester, NY 14642, USA
| | - Matthew A Smith
- Carnegie Mellon Neuroscience Institute, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Byron M Yu
- Carnegie Mellon Neuroscience Institute, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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35
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Rupprecht P, Carta S, Hoffmann A, Echizen M, Blot A, Kwan AC, Dan Y, Hofer SB, Kitamura K, Helmchen F, Friedrich RW. A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging. Nat Neurosci 2021; 24:1324-1337. [PMID: 34341584 PMCID: PMC7611618 DOI: 10.1038/s41593-021-00895-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.
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Affiliation(s)
- Peter Rupprecht
- Brain Research Institute, University of Zürich, Zurich, Switzerland.
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
| | - Stefano Carta
- Brain Research Institute, University of Zürich, Zurich, Switzerland
| | - Adrian Hoffmann
- Brain Research Institute, University of Zürich, Zurich, Switzerland
| | - Mayumi Echizen
- Department of Neurophysiology, University of Tokyo, Tokyo, Japan
- Department of Anesthesiology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Antonin Blot
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
- Biozentrum, University of Basel, Basel, Switzerland
| | - Alex C Kwan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Yang Dan
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley CA, USA
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
- Biozentrum, University of Basel, Basel, Switzerland
| | - Kazuo Kitamura
- Department of Neurophysiology, University of Tokyo, Tokyo, Japan
- Department of Neurophysiology, University of Yamanashi, Yamanashi, Japan
| | - Fritjof Helmchen
- Brain Research Institute, University of Zürich, Zurich, Switzerland.
| | - Rainer W Friedrich
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
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36
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Isbister JB, Reyes-Puerta V, Sun JJ, Horenko I, Luhmann HJ. Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo. Sci Rep 2021; 11:15066. [PMID: 34326363 PMCID: PMC8322153 DOI: 10.1038/s41598-021-94002-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 06/29/2021] [Indexed: 12/04/2022] Open
Abstract
How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further characterisation. Here we show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. On subsets of trials (clusters) and after controlling for stimulus-response adaptation, spike time differences between pairs of neurons are “time-warped” (compressed/stretched) by trial-to-trial changes in shared excitability, explaining why fixed spike time patterns and noise correlations are seldom reported. We show that predicted cortical state is correlated between groups of 4 neurons, introducing the possibility of spike time pattern modulation by population-wide trial-to-trial changes in excitability (i.e. cortical state). Under the assumption of state-dependent coding, we propose an improved potential encoding capacity.
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Affiliation(s)
- James B Isbister
- Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, Department of Experimental Psychology, University of Oxford, Oxford, UK. .,The Blue Brain Project, École Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland.
| | - Vicente Reyes-Puerta
- Institute of Physiology, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Jyh-Jang Sun
- Institute of Physiology, University Medical Center, Johannes Gutenberg University, Mainz, Germany.,NERF, Kapeldreef 75, 3001, Leuven, Belgium.,imec, Remisebosweg 1, 3001, Leuven, Belgium
| | - Illia Horenko
- Faculty of Informatics, Universita della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland
| | - Heiko J Luhmann
- Institute of Physiology, University Medical Center, Johannes Gutenberg University, Mainz, Germany
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37
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Weber AI, Shea-Brown E, Rieke F. Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions. eNeuro 2021; 8:ENEURO.0191-21.2021. [PMID: 34083382 PMCID: PMC8260275 DOI: 10.1523/eneuro.0191-21.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 05/10/2021] [Indexed: 11/21/2022] Open
Abstract
Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions.
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Affiliation(s)
- Alison I Weber
- Graduate Program in Neuroscience, University of Washington, Seattle, WA 98195
| | - Eric Shea-Brown
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195
| | - Fred Rieke
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195
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38
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Mell MM, St-Yves G, Naselaris T. Voxel-to-voxel predictive models reveal unexpected structure in unexplained variance. Neuroimage 2021; 238:118266. [PMID: 34129949 DOI: 10.1016/j.neuroimage.2021.118266] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 06/08/2021] [Accepted: 06/11/2021] [Indexed: 11/19/2022] Open
Abstract
Encoding models based on deep convolutional neural networks (DCNN) predict BOLD responses to natural scenes in the human visual system more accurately than many other currently available models. However, DCNN-based encoding models fail to predict a significant amount of variance in the activity of most voxels in all visual areas. This failure could reflect limitations in the data (e.g., a noise ceiling), or could reflect limitations of the DCNN as a model of computation in the brain. Understanding the source and structure of the unexplained variance could therefore provide helpful clues for improving models of brain computation. Here, we characterize the structure of the variance that DCNN-based encoding models cannot explain. Using a publicly available dataset of BOLD responses to natural scenes, we determined if the source of unexplained variance was shared across voxels, individual brains, retinotopic locations, and hierarchically distant visual brain areas. We answered these questions using voxel-to-voxel (vox2vox) models that predict activity in a target voxel given activity in a population of source voxels. We found that simple linear vox2vox models increased within-subject prediction accuracy over DCNN-based models for any pair of source/target visual areas, clearly demonstrating that the source of unexplained variance is widely shared within and across visual brain areas. However, vox2vox models were not more accurate than DCNN-based encoding models when source and target voxels came from different brains, demonstrating that the source of unexplained variance was not shared across brains. Importantly, control analyses demonstrated that the source of unexplained variance was not encoded in the mean activity of source voxels, or the activity of voxels in white matter. Interestingly, the weights of vox2vox models revealed preferential connection of target voxel activity to source voxels with adjacent receptive fields, even when source and target voxels were in different functional brain areas. Finally, we found that the prediction accuracy of the vox2vox models decayed with hierarchical distance between the source and target voxels but showed detailed patterns of dependence on hierarchical relationships that we did not observe in DCNNs. Given these results, we argue that the structured variance unexplained by DCNN-based encoding models is unlikely to be entirely caused by non-neural artifacts (e.g., spatially correlated measurement noise) or a failure of DCNNs to approximate the features encoded in brain activity; rather, our results point to a need for brain models that provide both mechanistic and computational explanations for structured ongoing activity in the brain. Keywords: fMRI, encoding models, deep neural networks, functional connectivity.
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Affiliation(s)
- Maggie Mae Mell
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Ghislain St-Yves
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Thomas Naselaris
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
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39
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Wolff A, Chen L, Tumati S, Golesorkhi M, Gomez-Pilar J, Hu J, Jiang S, Mao Y, Longtin A, Northoff G. Prestimulus dynamics blend with the stimulus in neural variability quenching. Neuroimage 2021; 238:118160. [PMID: 34058331 DOI: 10.1016/j.neuroimage.2021.118160] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/30/2021] [Accepted: 05/09/2021] [Indexed: 01/08/2023] Open
Abstract
Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.
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Affiliation(s)
- Annemarie Wolff
- University of Ottawa Institute of Mental Health Research, Ottawa, Canada.
| | - Liang Chen
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China.
| | - Shankar Tumati
- University of Ottawa Institute of Mental Health Research, Ottawa, Canada
| | - Mehrshad Golesorkhi
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red-Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain
| | - Jie Hu
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China
| | - Shize Jiang
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China
| | - Ying Mao
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China
| | - André Longtin
- Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada; Physics Department, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- University of Ottawa Institute of Mental Health Research, Ottawa, Canada; Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada
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40
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Wang Z, Chacron MJ. Synergistic population coding of natural communication stimuli by hindbrain electrosensory neurons. Sci Rep 2021; 11:10840. [PMID: 34035395 PMCID: PMC8149419 DOI: 10.1038/s41598-021-90413-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 05/11/2021] [Indexed: 01/11/2023] Open
Abstract
Understanding how neural populations encode natural stimuli with complex spatiotemporal structure to give rise to perception remains a central problem in neuroscience. Here we investigated population coding of natural communication stimuli by hindbrain neurons within the electrosensory system of weakly electric fish Apteronotus leptorhynchus. Overall, we found that simultaneously recorded neural activities were correlated: signal but not noise correlations were variable depending on the stimulus waveform as well as the distance between neurons. Combining the neural activities using an equal-weight sum gave rise to discrimination performance between different stimulus waveforms that was limited by redundancy introduced by noise correlations. However, using an evolutionary algorithm to assign different weights to individual neurons before combining their activities (i.e., a weighted sum) gave rise to increased discrimination performance by revealing synergistic interactions between neural activities. Our results thus demonstrate that correlations between the neural activities of hindbrain electrosensory neurons can enhance information about the structure of natural communication stimuli that allow for reliable discrimination between different waveforms by downstream brain areas.
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Affiliation(s)
- Ziqi Wang
- Department of Physiology, McGill University, Montreal, Canada
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41
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Baracchini G, Mišić B, Setton R, Mwilambwe-Tshilobo L, Girn M, Nomi JS, Uddin LQ, Turner GR, Spreng RN. Inter-regional BOLD signal variability is an organizational feature of functional brain networks. Neuroimage 2021; 237:118149. [PMID: 33991695 DOI: 10.1016/j.neuroimage.2021.118149] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/23/2021] [Accepted: 05/04/2021] [Indexed: 12/30/2022] Open
Abstract
Neuronal variability patterns promote the formation and organization of neural circuits. Macroscale similarities in regional variability patterns may therefore be linked to the strength and topography of inter-regional functional connections. To assess this relationship, we used multi-echo resting-state fMRI and investigated macroscale connectivity-variability associations in 154 adult humans (86 women; mean age = 22yrs). We computed inter-regional measures of moment-to-moment BOLD signal variability and related them to inter-regional functional connectivity. Region pairs that showed stronger functional connectivity also showed similar BOLD signal variability patterns, independent of inter-regional distance and structural similarity. Connectivity-variability associations were predominant within all networks and followed a hierarchical spatial organization that separated sensory, motor and attention systems from limbic, default and frontoparietal control association networks. Results were replicated in a second held-out fMRI run. These findings suggest that macroscale BOLD signal variability is an organizational feature of large-scale functional networks, and shared inter-regional BOLD signal variability may underlie macroscale brain network dynamics.
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Affiliation(s)
- Giulia Baracchini
- Department of Neurology and Neurosurgery, Montréal Neurological Institute, Montréal, QC H3A 2B4, Canada; Douglas Mental Health University Institute, Montréal, QC H4H 1R3, Canada.
| | - Bratislav Mišić
- Department of Neurology and Neurosurgery, Montréal Neurological Institute, Montréal, QC H3A 2B4, Canada; McConnell Brain Imaging Center, McGill University, Montréal, QC H3A 2B4, Canada
| | - Roni Setton
- Department of Neurology and Neurosurgery, Montréal Neurological Institute, Montréal, QC H3A 2B4, Canada
| | | | - Manesh Girn
- Department of Neurology and Neurosurgery, Montréal Neurological Institute, Montréal, QC H3A 2B4, Canada
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL 33146, Canada
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33146, Canada
| | - Gary R Turner
- Department of Psychology, York University, Toronto, ON M3J 1P3, Canada
| | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montréal Neurological Institute, Montréal, QC H3A 2B4, Canada; Douglas Mental Health University Institute, Montréal, QC H4H 1R3, Canada; McConnell Brain Imaging Center, McGill University, Montréal, QC H3A 2B4, Canada; Departments of Psychiatry and Psychology, McGill University, Montréal, QC H3A 1G1, Canada.
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42
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Hennig JA, Oby ER, Golub MD, Bahureksa LA, Sadtler PT, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Chase SM, Yu BM. Learning is shaped by abrupt changes in neural engagement. Nat Neurosci 2021; 24:727-736. [PMID: 33782622 DOI: 10.1038/s41593-021-00822-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 02/22/2021] [Indexed: 01/30/2023]
Abstract
Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.
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Affiliation(s)
- Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA. .,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA. .,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Lindsay A Bahureksa
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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43
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Metzen MG, Chacron MJ. Population Coding of Natural Electrosensory Stimuli by Midbrain Neurons. J Neurosci 2021; 41:3822-3841. [PMID: 33687962 PMCID: PMC8084312 DOI: 10.1523/jneurosci.2232-20.2021] [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: 08/25/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/27/2022] Open
Abstract
Natural stimuli display spatiotemporal characteristics that typically vary over orders of magnitude, and their encoding by sensory neurons remains poorly understood. We investigated population coding of highly heterogeneous natural electrocommunication stimuli in Apteronotus leptorhynchus of either sex. Neuronal activities were positively correlated with one another in the absence of stimulation, and correlation magnitude decayed with increasing distance between recording sites. Under stimulation, we found that correlations between trial-averaged neuronal responses (i.e., signal correlations) were positive and higher in magnitude for neurons located close to another, but that correlations between the trial-to-trial variability (i.e., noise correlations) were independent of physical distance. Overall, signal and noise correlations were independent of stimulus waveform as well as of one another. To investigate how neuronal populations encoded natural electrocommunication stimuli, we considered a nonlinear decoder for which the activities were combined. Decoding performance was best for a timescale of 6 ms, indicating that midbrain neurons transmit information via precise spike timing. A simple summation of neuronal activities (equally weighted sum) revealed that noise correlations limited decoding performance by introducing redundancy. Using an evolution algorithm to optimize performance when considering instead unequally weighted sums of neuronal activities revealed much greater performance values, indicating that midbrain neuron populations transmit information that reliably enable discrimination between different stimulus waveforms. Interestingly, we found that different weight combinations gave rise to similar discriminability, suggesting robustness. Our results have important implications for understanding how natural stimuli are integrated by downstream brain areas to give rise to behavioral responses.SIGNIFICANCE STATEMENT We show that midbrain electrosensory neurons display correlations between their activities and that these can significantly impact performance of decoders. While noise correlations limited discrimination performance by introducing redundancy, considering unequally weighted sums of neuronal activities gave rise to much improved performance and mitigated the deleterious effects of noise correlations. Further analysis revealed that increased discriminability was achieved by making trial-averaged responses more separable, as well as by reducing trial-to-trial variability by eliminating noise correlations. We further found that multiple combinations of weights could give rise to similar discrimination performances, which suggests that such combinatorial codes could be achieved in the brain. We conclude that the activities of midbrain neuronal populations can be used to reliably discriminate between highly heterogeneous stimulus waveforms.
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Affiliation(s)
- Michael G Metzen
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Maurice J Chacron
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
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44
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Azeredo da Silveira R, Rieke F. The Geometry of Information Coding in Correlated Neural Populations. Annu Rev Neurosci 2021; 44:403-424. [PMID: 33863252 DOI: 10.1146/annurev-neuro-120320-082744] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code.
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Affiliation(s)
| | - Fred Rieke
- Department of Physics, Ecole Normale Supérieure, 75005 Paris, France;
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45
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Koay SA, Thiberge S, Brody CD, Tank DW. Amplitude modulations of cortical sensory responses in pulsatile evidence accumulation. eLife 2020; 9:e60628. [PMID: 33263278 PMCID: PMC7811404 DOI: 10.7554/elife.60628] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 11/30/2020] [Indexed: 12/27/2022] Open
Abstract
How does the brain internally represent a sequence of sensory information that jointly drives a decision-making behavior? Studies of perceptual decision-making have often assumed that sensory cortices provide noisy but otherwise veridical sensory inputs to downstream processes that accumulate and drive decisions. However, sensory processing in even the earliest sensory cortices can be systematically modified by various external and internal contexts. We recorded from neuronal populations across posterior cortex as mice performed a navigational decision-making task based on accumulating randomly timed pulses of visual evidence. Even in V1, only a small fraction of active neurons had sensory-like responses time-locked to each pulse. Here, we focus on how these 'cue-locked' neurons exhibited a variety of amplitude modulations from sensory to cognitive, notably by choice and accumulated evidence. These task-related modulations affected a large fraction of cue-locked neurons across posterior cortex, suggesting that future models of behavior should account for such influences.
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Affiliation(s)
- Sue Ann Koay
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Stephan Thiberge
- Bezos Center for Neural Circuit Dynamics, Princeton UniversityPrincetonUnited States
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Howard Hughes Medical Institute, Princeton UniversityPrincetonUnited States
| | - David W Tank
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Bezos Center for Neural Circuit Dynamics, Princeton UniversityPrincetonUnited States
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46
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Ruff DA, Xue C, Kramer LE, Baqai F, Cohen MR. Low rank mechanisms underlying flexible visual representations. Proc Natl Acad Sci U S A 2020; 117:29321-29329. [PMID: 33229536 PMCID: PMC7703603 DOI: 10.1073/pnas.2005797117] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Neuronal population responses to sensory stimuli are remarkably flexible. The responses of neurons in visual cortex have heterogeneous dependence on stimulus properties (e.g., contrast), processes that affect all stages of visual processing (e.g., adaptation), and cognitive processes (e.g., attention or task switching). Understanding whether these processes affect similar neuronal populations and whether they have similar effects on entire populations can provide insight into whether they utilize analogous mechanisms. In particular, it has recently been demonstrated that attention has low rank effects on the covariability of populations of visual neurons, which impacts perception and strongly constrains mechanistic models. We hypothesized that measuring changes in population covariability associated with other sensory and cognitive processes could clarify whether they utilize similar mechanisms or computations. Our experimental design included measurements in multiple visual areas using four distinct sensory and cognitive processes. We found that contrast, adaptation, attention, and task switching affect the variability of responses of populations of neurons in primate visual cortex in a similarly low rank way. These results suggest that a given circuit may use similar mechanisms to perform many forms of modulation and likely reflects a general principle that applies to a wide range of brain areas and sensory, cognitive, and motor processes.
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Affiliation(s)
- Douglas A Ruff
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
| | - Cheng Xue
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
| | - Lily E Kramer
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
| | - Faisal Baqai
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15260
| | - Marlene R Cohen
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260;
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15260
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47
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Cowley BR, Snyder AC, Acar K, Williamson RC, Yu BM, Smith MA. Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex. Neuron 2020; 108:551-567.e8. [PMID: 32810433 PMCID: PMC7822647 DOI: 10.1016/j.neuron.2020.07.021] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 06/15/2020] [Accepted: 07/17/2020] [Indexed: 12/22/2022]
Abstract
An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.
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Affiliation(s)
- Benjamin R Cowley
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Adam C Snyder
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA; Department of Neuroscience, University of Rochester, Rochester, NY 14642, USA; Center for Visual Science, University of Rochester, Rochester, NY 14642, USA
| | - Katerina Acar
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ryan C Williamson
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA; University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Byron M Yu
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Matthew A Smith
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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48
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Xia J, Marks TD, Goard MJ, Wessel R. Diverse coactive neurons encode stimulus-driven and stimulus-independent variables. J Neurophysiol 2020; 124:1505-1517. [PMID: 32965146 DOI: 10.1152/jn.00431.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Both experimenter-controlled stimuli and stimulus-independent variables impact cortical neural activity. A major hurdle to understanding neural representation is distinguishing between qualitatively different causes of the fluctuating population activity. We applied an unsupervised low-rank tensor decomposition analysis to the recorded population activity in the visual cortex of awake mice in response to repeated presentations of naturalistic visual stimuli. We found that neurons covaried largely independently of individual neuron stimulus response reliability and thus encoded both stimulus-driven and stimulus-independent variables. Importantly, a neuron's response reliability and the neuronal coactivation patterns substantially reorganized for different external visual inputs. Analysis of recurrent balanced neural network models revealed that both the stimulus specificity and the mixed encoding of qualitatively different variables can arise from clustered external inputs. These results establish that coactive neurons with diverse response reliability mediate a mixed representation of stimulus-driven and stimulus-independent variables in the visual cortex.NEW & NOTEWORTHY V1 neurons covary largely independently of individual neuron's response reliability. A single neuron's response reliability imposes only a weak constraint on its encoding capabilities. Visual stimulus instructs a neuron's reliability and coactivation pattern. Network models revealed using clustered external inputs.
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Affiliation(s)
- Ji Xia
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri
| | - Tyler D Marks
- Neuroscience Research Institute, University of California, Santa Barbara, California
| | - Michael J Goard
- Neuroscience Research Institute, University of California, Santa Barbara, California.,Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, California.,Department of Psychological & Brain Sciences, University of California, Santa Barbara, California
| | - Ralf Wessel
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri
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49
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Triplett MA, Pujic Z, Sun B, Avitan L, Goodhill GJ. Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data. PLoS Comput Biol 2020; 16:e1008330. [PMID: 33253161 PMCID: PMC7728401 DOI: 10.1371/journal.pcbi.1008330] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/10/2020] [Accepted: 09/10/2020] [Indexed: 11/19/2022] Open
Abstract
The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.
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Affiliation(s)
- Marcus A. Triplett
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
- School of Mathematics and Physics, The University of Queensland, St Lucia, Australia
| | - Zac Pujic
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
| | - Biao Sun
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
| | - Lilach Avitan
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
| | - Geoffrey J. Goodhill
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
- School of Mathematics and Physics, The University of Queensland, St Lucia, Australia
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50
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Christensen RK, Lindén H, Nakamura M, Barkat TR. White Noise Background Improves Tone Discrimination by Suppressing Cortical Tuning Curves. Cell Rep 2020; 29:2041-2053.e4. [PMID: 31722216 DOI: 10.1016/j.celrep.2019.10.049] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 07/25/2019] [Accepted: 10/09/2019] [Indexed: 12/23/2022] Open
Abstract
The brain faces the difficult task of maintaining a stable representation of key features of the outside world in noisy sensory surroundings. How does the sensory representation change with noise, and how does the brain make sense of it? We investigated the effect of background white noise (WN) on tuning properties of neurons in mouse A1 and its impact on discrimination performance in a go/no-go task. We find that WN suppresses the activity of A1 neurons, which surprisingly increases the discriminability of tones spectrally close to each other. To confirm the involvement of A1, we optogenetically excited parvalbumin-positive (PV+) neurons in A1, which have similar effects as WN on both tuning properties and frequency discrimination. A population model suggests that the suppression of A1 tuning curves increases frequency selectivity and thereby improves discrimination. Our findings demonstrate that the cortical representation of pure tones adapts during noise to improve sensory acuity.
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Affiliation(s)
- Rasmus Kordt Christensen
- Department of Biomedicine, Basel University, 4056 Basel, Switzerland; Department of Neuroscience, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Henrik Lindén
- Department of Neuroscience, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Mari Nakamura
- Department of Biomedicine, Basel University, 4056 Basel, Switzerland
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