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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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2
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Moseley SM, Meliza CD. Cortical Processing of Conspecific Vocalizations in Zebra Finches Depends on the Early Acoustical Environment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600670. [PMID: 38979160 PMCID: PMC11230381 DOI: 10.1101/2024.06.25.600670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Sensory experience during development has lasting effects on perception and neural processing. Exposing animals to artificial stimuli early in life influences the tuning and functional organization of the auditory cortex, but less is known about how the rich acoustical environments experienced by vocal communicators affect the processing of complex vocalizations. Here, we show that in zebra finches (Taeniopygia guttata), a colonial-breeding songbird species, exposure to a naturalistic social-acoustical environment during development has a profound impact on cortical-level auditory responses to conspecific song. Compared to birds raised by pairs in acoustic isolation, birds raised in a breeding colony had higher average firing rates, selectivity, and discriminability, especially in the narrow-spiking, putatively inhibitory neurons of a higher-order auditory area, the caudomedial nidopallium (NCM). Neurons in colony-reared birds were also less correlated in their tuning and more efficient at encoding the spectrotemporal structure of conspecific song. These results suggest that the auditory cortex adapts to noisy, complex acoustical environments by strengthening inhibitory circuitry, functionally decoupling excitatory neurons while maintaining overall excitatory-inhibitory balance.
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Affiliation(s)
- Samantha M Moseley
- Department of Psychology, University of Virginia, Charlottesville VA 22904, USA
| | - C Daniel Meliza
- Department of Psychology, University of Virginia, Charlottesville VA 22904, USA
- Neuroscience Graduate Program, University of Virginia, Charlottesville VA 22904, USA
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3
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Desowska A, Coffman S, Kim I, Underwood E, Tao A, Lopez KL, Nelson CA, Hensch TK, Gabard-Durnam L, Cornelissen L, Berde CB. Neurodevelopment of children exposed to prolonged anesthesia in infancy: GABA study interim analysis of resting-state brain networks at 2, 4, and 10-months old. Neuroimage Clin 2024; 42:103614. [PMID: 38754325 PMCID: PMC11126539 DOI: 10.1016/j.nicl.2024.103614] [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: 01/30/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Previous studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants. METHODS EEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure. RESULTS Models showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05-0.34). An effect of age was apparent in many of these measures. CONCLUSIONS We could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.
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Affiliation(s)
- Adela Desowska
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Siobhan Coffman
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Isabelle Kim
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Ellen Underwood
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Alice Tao
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Kelsie L Lopez
- Center for Cognitive and Brain Health, Northeastern University, Boston, USA
| | - Charles A Nelson
- Laboratories of Cognitive Neurosciences, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Takao K Hensch
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | | | - Laura Cornelissen
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Charles B Berde
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA.
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4
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Goris RLT, Coen-Cagli R, Miller KD, Priebe NJ, Lengyel M. Response sub-additivity and variability quenching in visual cortex. Nat Rev Neurosci 2024; 25:237-252. [PMID: 38374462 PMCID: PMC11444047 DOI: 10.1038/s41583-024-00795-0] [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] [Accepted: 01/24/2024] [Indexed: 02/21/2024]
Abstract
Sub-additivity and variability are ubiquitous response motifs in the primary visual cortex (V1). Response sub-additivity enables the construction of useful interpretations of the visual environment, whereas response variability indicates the factors that limit the precision with which the brain can do this. There is increasing evidence that experimental manipulations that elicit response sub-additivity often also quench response variability. Here, we provide an overview of these phenomena and suggest that they may have common origins. We discuss empirical findings and recent model-based insights into the functional operations, computational objectives and circuit mechanisms underlying V1 activity. These different modelling approaches all predict that response sub-additivity and variability quenching often co-occur. The phenomenology of these two response motifs, as well as many of the insights obtained about them in V1, generalize to other cortical areas. Thus, the connection between response sub-additivity and variability quenching may be a canonical motif across the cortex.
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Affiliation(s)
- Robbe L T Goris
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Dominick P. 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
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Dept. of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Swartz Program in Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Nicholas J Priebe
- Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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5
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Noda T, Takahashi H. Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat. Cereb Cortex 2024; 34:bhad428. [PMID: 37955660 PMCID: PMC10793590 DOI: 10.1093/cercor/bhad428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 10/10/2023] [Accepted: 10/25/2023] [Indexed: 11/14/2023] Open
Abstract
The awake cortex is characterized by a higher level of ongoing spontaneous activity, but it has a better detectability of weak sensory inputs than the anesthetized cortex. However, the computational mechanism underlying this paradoxical nature of awake neuronal activity remains to be elucidated. Here, we propose a hypothetical stochastic resonance, which improves the signal-to-noise ratio (SNR) of weak sensory inputs through nonlinear relations between ongoing spontaneous activities and sensory-evoked activities. Prestimulus and tone-evoked activities were investigated via in vivo extracellular recording with a dense microelectrode array covering the entire auditory cortex in rats in both awake and anesthetized states. We found that tone-evoked activities increased supralinearly with the prestimulus activity level in the awake state and that the SNR of weak stimulus representation was optimized at an intermediate level of prestimulus ongoing activity. Furthermore, the temporally intermittent firing pattern, but not the trial-by-trial reliability or the fluctuation of local field potential, was identified as a relevant factor for SNR improvement. Since ongoing activity differs among neurons, hypothetical stochastic resonance or "sparse network stochastic resonance" might offer beneficial SNR improvement at the single-neuron level, which is compatible with the sparse representation in the sensory cortex.
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Affiliation(s)
- Takahiro Noda
- Department of Mechano-informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Hirokazu Takahashi
- Department of Mechano-informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
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6
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Zhang WH, Wu S, Josić K, Doiron B. Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons. Nat Commun 2023; 14:7074. [PMID: 37925497 PMCID: PMC10625605 DOI: 10.1038/s41467-023-41743-3] [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: 01/31/2022] [Accepted: 09/15/2023] [Indexed: 11/06/2023] Open
Abstract
Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.
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Affiliation(s)
- Wen-Hao Zhang
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Si Wu
- School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
- Center of Quantitative Biology, Peking University, Beijing, 100871, China
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, TX, USA.
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA.
| | - Brent Doiron
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA.
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA.
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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7
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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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8
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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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9
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Cnudde K, Kim G, Murch WS, Handy TC, Protzner AB, Kam JWY. EEG complexity during mind wandering: A multiscale entropy investigation. Neuropsychologia 2023; 180:108480. [PMID: 36621593 DOI: 10.1016/j.neuropsychologia.2023.108480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 10/16/2022] [Accepted: 01/05/2023] [Indexed: 01/07/2023]
Abstract
Our attention often drifts away from the ongoing task to task-unrelated thoughts, a phenomenon commonly referred to as mind wandering. Ample studies dedicated to delineating its electrophysiological correlates have revealed distinct event-related potentials (ERP) and spectral patterns associated with mind wandering. It remains less clear whether the complexity of the electroencephalography (EEG) changes when our minds wander, a metric that captures the predictability of the time series at varying timescales. Accordingly, this study investigated whether mind wandering impacts EEG signal complexity. We further explored whether such effects differ across timescales, and change in a context-dependent manner as indexed by global and local levels of processing. To address this, we recorded participants' EEG while they completed Navon's global and local processing task and occasionally reported whether they were on-task or mind wandering throughout the task. We found that brain signal complexity as indexed by multiscale entropy decreased at medium timescales in centro-parietal regions and increased at coarse timescales in anterior and posterior regions during mind wandering, as compared to the on-task state, for global processing. Moreover, global processing showed increased complexity at fine to medium timescales compared to local processing. Finally, behavioral performance revealed a context-dependent effect in accuracy measures, with mind wandering showing lower accuracy compared to the on-task state only during the local condition. Taken together, these results indicate that changes in brain signal complexity across timescales may be an important feature of mind wandering.
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Affiliation(s)
- Kelsey Cnudde
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4.
| | - Gahyun Kim
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4
| | - W Spencer Murch
- Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, British Columbia, Canada, V6T 1Z4; Department of Sociology & Anthropology, Concordia University, 1455 de Maisonneuve Blvd W, Montreal, Quebec, Canada, H3G 1M8
| | - Todd C Handy
- Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, British Columbia, Canada, V6T 1Z4
| | - Andrea B Protzner
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4; Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada, T2N 1N4; Mathison Centre for Mental Health, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta, Canada, T2N 4Z6
| | - Julia W Y Kam
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4; Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, British Columbia, Canada, V6T 1Z4; Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada, T2N 1N4
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10
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Nurminen L, Bijanzadeh M, Angelucci A. Size tuning of neural response variability in laminar circuits of macaque primary visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.17.524397. [PMID: 36711786 PMCID: PMC9882156 DOI: 10.1101/2023.01.17.524397] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
A defining feature of the cortex is its laminar organization, which is likely critical for cortical information processing. For example, visual stimuli of different size evoke distinct patterns of laminar activity. Visual information processing is also influenced by the response variability of individual neurons and the degree to which this variability is correlated among neurons. To elucidate laminar processing, we studied how neural response variability across the layers of macaque primary visual cortex is modulated by visual stimulus size. Our laminar recordings revealed that single neuron response variability and the shared variability among neurons are tuned for stimulus size, and this size-tuning is layer-dependent. In all layers, stimulation of the receptive field (RF) reduced single neuron variability, and the shared variability among neurons, relative to their pre-stimulus values. As the stimulus was enlarged beyond the RF, both single neuron and shared variability increased in supragranular layers, but either did not change or decreased in other layers. Surprisingly, we also found that small visual stimuli could increase variability relative to baseline values. Our results suggest multiple circuits and mechanisms as the source of variability in different layers and call for the development of new models of neural response variability.
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Affiliation(s)
- Lauri Nurminen
- Department of Ophthalmology and Visual Science, Moran Eye Institute, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
- Present address: College of Optometry, University of Houston, 4401 Martin Luther King Boulevard, Houston, TX 77204-2020, USA
| | - Maryam Bijanzadeh
- Department of Ophthalmology and Visual Science, Moran Eye Institute, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Alessandra Angelucci
- Department of Ophthalmology and Visual Science, Moran Eye Institute, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
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11
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Chadwick A, Khan AG, Poort J, Blot A, Hofer SB, Mrsic-Flogel TD, Sahani M. Learning shapes cortical dynamics to enhance integration of relevant sensory input. Neuron 2023; 111:106-120.e10. [PMID: 36283408 PMCID: PMC7614688 DOI: 10.1016/j.neuron.2022.10.001] [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: 08/01/2021] [Revised: 07/14/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.
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Affiliation(s)
- Angus Chadwick
- Gatsby Computational Neuroscience Unit, University College London, London, UK; Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Adil G Khan
- Centre for Developmental Neurobiology, King's College London, London, UK
| | - Jasper Poort
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Antonin Blot
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Thomas D Mrsic-Flogel
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK.
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12
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Lundqvist M, Rose J, Brincat SL, Warden MR, Buschman TJ, Herman P, Miller EK. Reduced variability of bursting activity during working memory. Sci Rep 2022; 12:15050. [PMID: 36064880 PMCID: PMC9445015 DOI: 10.1038/s41598-022-18577-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/16/2022] [Indexed: 12/03/2022] Open
Abstract
Working memories have long been thought to be maintained by persistent spiking. However, mounting evidence from multiple-electrode recording (and single-trial analyses) shows that the underlying spiking is better characterized by intermittent bursts of activity. A counterargument suggested this intermittent activity is at odds with observations that spike-time variability reduces during task performance. However, this counterargument rests on assumptions, such as randomness in the timing of the bursts, which may not be correct. Thus, we analyzed spiking and LFPs from monkeys' prefrontal cortex (PFC) to determine if task-related reductions in variability can co-exist with intermittent spiking. We found that it does because both spiking and associated gamma bursts were task-modulated, not random. In fact, the task-related reduction in spike variability could largely be explained by a related reduction in gamma burst variability. Our results provide further support for the intermittent activity models of working memory as well as novel mechanistic insights into how spike variability is reduced during cognitive tasks.
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Affiliation(s)
- Mikael Lundqvist
- Department of Psychology, Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden.
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Jonas Rose
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Faculty of Psychology, Neural Basis of Learning, Ruhr University Bochum, 44801, Bochum, Germany
| | - Scott L Brincat
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Melissa R Warden
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Timothy J Buschman
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ, 08540, USA
| | - Pawel Herman
- Department of Computational Science and Technology, School of Electrical Engineering and Computer Science and Digital Futures, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden
| | - Earl K Miller
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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13
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Qi Y, Gong P. Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits. Nat Commun 2022; 13:4572. [PMID: 35931698 PMCID: PMC9356069 DOI: 10.1038/s41467-022-32279-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 07/22/2022] [Indexed: 11/08/2022] Open
Abstract
A range of perceptual and cognitive processes have been characterized from the perspective of probabilistic representations and inference. To understand the neural circuit mechanism underlying these probabilistic computations, we develop a theory based on complex spatiotemporal dynamics of neural population activity. We first implement and explore this theory in a biophysically realistic, spiking neural circuit. Population activity patterns emerging from the circuit capture realistic variability or fluctuations of neural dynamics both in time and in space. These activity patterns implement a type of probabilistic computations that we name fractional neural sampling (FNS). We further develop a mathematical model to reveal the algorithmic nature of FNS and its computational advantages for representing multimodal distributions, a major challenge faced by existing theories. We demonstrate that FNS provides a unified account of a diversity of experimental observations of neural spatiotemporal dynamics and perceptual processes such as visual perception inference, and that FNS makes experimentally testable predictions.
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Affiliation(s)
- Yang Qi
- School of Physics, University of Sydney, Sydney, NSW, 2006, Australia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW, 2006, Australia.
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14
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Zhang L, Prince SM, Paulson AL, Singer AC. Goal discrimination in hippocampal nonplace cells when place information is ambiguous. Proc Natl Acad Sci U S A 2022; 119:e2107337119. [PMID: 35254897 PMCID: PMC8931233 DOI: 10.1073/pnas.2107337119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 01/30/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceGoal-directed spatial navigation has been found to rely on hippocampal neurons that are spatially modulated. We show that "nonplace" cells without significant spatial modulation play a role in discriminating goals when environmental cues for goals are ambiguous. This nonplace cell activity is performance-dependent and is modulated by gamma oscillations. Finally, nonplace cell goal discrimination coding fails in a mouse model of Alzheimer's disease (AD). Together, these results show that nonplace cell firing can signal unique task-relevant information when spatial information is ambiguous; these signals depend on performance and are absent in a mouse model of AD.
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Affiliation(s)
- Lu Zhang
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Stephanie M. Prince
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA 30322
| | - Abigail L. Paulson
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Annabelle C. Singer
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
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15
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Wu YK, Zenke F. Nonlinear transient amplification in recurrent neural networks with short-term plasticity. eLife 2021; 10:e71263. [PMID: 34895468 PMCID: PMC8820736 DOI: 10.7554/elife.71263] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 12/10/2021] [Indexed: 11/24/2022] Open
Abstract
To rapidly process information, neural circuits have to amplify specific activity patterns transiently. How the brain performs this nonlinear operation remains elusive. Hebbian assemblies are one possibility whereby strong recurrent excitatory connections boost neuronal activity. However, such Hebbian amplification is often associated with dynamical slowing of network dynamics, non-transient attractor states, and pathological run-away activity. Feedback inhibition can alleviate these effects but typically linearizes responses and reduces amplification gain. Here, we study nonlinear transient amplification (NTA), a plausible alternative mechanism that reconciles strong recurrent excitation with rapid amplification while avoiding the above issues. NTA has two distinct temporal phases. Initially, positive feedback excitation selectively amplifies inputs that exceed a critical threshold. Subsequently, short-term plasticity quenches the run-away dynamics into an inhibition-stabilized network state. By characterizing NTA in supralinear network models, we establish that the resulting onset transients are stimulus selective and well-suited for speedy information processing. Further, we find that excitatory-inhibitory co-tuning widens the parameter regime in which NTA is possible in the absence of persistent activity. In summary, NTA provides a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.
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Affiliation(s)
- Yue Kris Wu
- Friedrich Miescher Institute for Biomedical ResearchBaselSwitzerland
- Faculty of Natural Sciences, University of BaselBaselSwitzerland
- Max Planck Institute for Brain ResearchFrankfurtGermany
- School of Life Sciences, Technical University of MunichFreisingGermany
| | - Friedemann Zenke
- Friedrich Miescher Institute for Biomedical ResearchBaselSwitzerland
- Faculty of Natural Sciences, University of BaselBaselSwitzerland
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16
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Abstract
Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.
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17
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Adibi M, Lampl I. Sensory Adaptation in the Whisker-Mediated Tactile System: Physiology, Theory, and Function. Front Neurosci 2021; 15:770011. [PMID: 34776857 PMCID: PMC8586522 DOI: 10.3389/fnins.2021.770011] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 09/30/2021] [Indexed: 12/03/2022] Open
Abstract
In the natural environment, organisms are constantly exposed to a continuous stream of sensory input. The dynamics of sensory input changes with organism's behaviour and environmental context. The contextual variations may induce >100-fold change in the parameters of the stimulation that an animal experiences. Thus, it is vital for the organism to adapt to the new diet of stimulation. The response properties of neurons, in turn, dynamically adjust to the prevailing properties of sensory stimulation, a process known as "neuronal adaptation." Neuronal adaptation is a ubiquitous phenomenon across all sensory modalities and occurs at different stages of processing from periphery to cortex. In spite of the wealth of research on contextual modulation and neuronal adaptation in visual and auditory systems, the neuronal and computational basis of sensory adaptation in somatosensory system is less understood. Here, we summarise the recent finding and views about the neuronal adaptation in the rodent whisker-mediated tactile system and further summarise the functional effect of neuronal adaptation on the response dynamics and encoding efficiency of neurons at single cell and population levels along the whisker-mediated touch system in rodents. Based on direct and indirect pieces of evidence presented here, we suggest sensory adaptation provides context-dependent functional mechanisms for noise reduction in sensory processing, salience processing and deviant stimulus detection, shift between integration and coincidence detection, band-pass frequency filtering, adjusting neuronal receptive fields, enhancing neural coding and improving discriminability around adapting stimuli, energy conservation, and disambiguating encoding of principal features of tactile stimuli.
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Affiliation(s)
- Mehdi Adibi
- Department of Physiology and Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Ilan Lampl
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
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18
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Ly C, Barreiro AK, Gautam SH, Shew WL. Odor-evoked increases in olfactory bulb mitral cell spiking variability. iScience 2021; 24:102946. [PMID: 34485855 PMCID: PMC8397902 DOI: 10.1016/j.isci.2021.102946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/07/2021] [Accepted: 08/02/2021] [Indexed: 01/04/2023] Open
Abstract
The spiking variability of neural networks has important implications for how information is encoded to higher brain regions. It has been well documented by numerous labs in many cortical and motor regions that spiking variability decreases with stimulus onset, yet whether this principle holds in the OB has not been tested. In stark contrast to this common view, we demonstrate that the onset of sensory input can cause an increase in the variability of neural activity in the mammalian OB. We show this in both anesthetized and awake rodents. Furthermore, we use computational models to describe the mechanisms of this phenomenon. Our findings establish sensory evoked increases in spiking variability as a viable alternative coding strategy.
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Affiliation(s)
- Cheng Ly
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Andrea K. Barreiro
- Department of Mathematics, Southern Methodist University, Dallas, TX 75275, USA
| | - Shree Hari Gautam
- Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA
| | - Woodrow L. Shew
- Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA
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19
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Carriot J, Cullen KE, Chacron MJ. The neural basis for violations of Weber's law in self-motion perception. Proc Natl Acad Sci U S A 2021; 118:e2025061118. [PMID: 34475203 PMCID: PMC8433496 DOI: 10.1073/pnas.2025061118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 06/25/2021] [Indexed: 01/18/2023] Open
Abstract
A prevailing view is that Weber's law constitutes a fundamental principle of perception. This widely accepted psychophysical law states that the minimal change in a given stimulus that can be perceived increases proportionally with amplitude and has been observed across systems and species in hundreds of studies. Importantly, however, Weber's law is actually an oversimplification. Notably, there exist violations of Weber's law that have been consistently observed across sensory modalities. Specifically, perceptual performance is better than that predicted from Weber's law for the higher stimulus amplitudes commonly found in natural sensory stimuli. To date, the neural mechanisms mediating such violations of Weber's law in the form of improved perceptual performance remain unknown. Here, we recorded from vestibular thalamocortical neurons in rhesus monkeys during self-motion stimulation. Strikingly, we found that neural discrimination thresholds initially increased but saturated for higher stimulus amplitudes, thereby causing the improved neural discrimination performance required to explain perception. Theory predicts that stimulus-dependent neural variability and/or response nonlinearities will determine discrimination threshold values. Using computational methods, we thus investigated the mechanisms mediating this improved performance. We found that the structure of neural variability, which initially increased but saturated for higher amplitudes, caused improved discrimination performance rather than response nonlinearities. Taken together, our results reveal the neural basis for violations of Weber's law and further provide insight as to how variability contributes to the adaptive encoding of natural stimuli with continually varying statistics.
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Affiliation(s)
- Jerome Carriot
- Department of Physiology, McGill University, Montréal, QC H3G 1Y6, Canada
| | - Kathleen E Cullen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218
| | - Maurice J Chacron
- Department of Physiology, McGill University, Montréal, QC H3G 1Y6, Canada;
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20
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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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21
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Konno D, Nishimoto S, Suzuki T, Ikegaya Y, Matsumoto N. Multiple states in ongoing neural activity in the rat visual cortex. PLoS One 2021; 16:e0256791. [PMID: 34437630 PMCID: PMC8389421 DOI: 10.1371/journal.pone.0256791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 08/16/2021] [Indexed: 01/04/2023] Open
Abstract
The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive. Using a custom-made electrode array, we recorded 32-site electrocorticograms in the primary and secondary visual cortex of freely behaving rats and determined the propagation patterns of spontaneous neural activity. Nonlinear dimensionality reduction and unsupervised clustering revealed multiple discrete states of the activity patterns. The activity remained stable in one state and suddenly jumped to another state. The diversity and dynamics of the internally switching cortical states would imply flexibility of neural responses to various external inputs.
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Affiliation(s)
- Daichi Konno
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shinji Nishimoto
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, Japan
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, Japan
| | - Yuji Ikegaya
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, Japan
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan
| | - Nobuyoshi Matsumoto
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
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22
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Festa D, Aschner A, Davila A, Kohn A, Coen-Cagli R. Neuronal variability reflects probabilistic inference tuned to natural image statistics. Nat Commun 2021; 12:3635. [PMID: 34131142 PMCID: PMC8206154 DOI: 10.1038/s41467-021-23838-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 05/19/2021] [Indexed: 11/23/2022] Open
Abstract
Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses-their variability-can be explained by a probabilistic representation tuned to naturalistic inputs.
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Affiliation(s)
- Dylan Festa
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Amir Aschner
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Aida Davila
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Adam Kohn
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- 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
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA.
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.
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23
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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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24
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Chicharro D, Panzeri S, Haefner RM. Stimulus-dependent relationships between behavioral choice and sensory neural responses. eLife 2021; 10:e54858. [PMID: 33825683 PMCID: PMC8184215 DOI: 10.7554/elife.54858] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/06/2021] [Indexed: 01/16/2023] Open
Abstract
Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.
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Affiliation(s)
- Daniel Chicharro
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di TecnologiaRoveretoItaly
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di TecnologiaRoveretoItaly
| | - Ralf M Haefner
- Brain and Cognitive Sciences, Center for Visual Science, University of RochesterRochesterUnited States
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25
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Parto Dezfouli M, Zarei M, Constantinidis C, Daliri MR. Task-specific modulation of PFC activity for matching-rule governed decision-making. Brain Struct Funct 2021; 226:443-455. [PMID: 33398431 DOI: 10.1007/s00429-020-02191-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 11/27/2020] [Indexed: 01/08/2023]
Abstract
Storing information from incoming stimuli in working memory (WM) is essential for decision-making. The prefrontal cortex (PFC) plays a key role to support this process. Previous studies have characterized different neuronal populations in the PFC for working memory judgements based on whether an originally presented stimulus matches a subsequently presented one (matching-rule decision-making). However, much remains to be understood about this mechanism at the population level of PFC neurons. Here, we hypothesized differences in processing of feature vs. spatial WM within the PFC during a matching-rule decision-making task. To test this hypothesis, the modulation of neural activity within the PFC during two types of decision-making tasks (spatial WM and feature WM) in comparison to a passive fixation task was determined. We discovered that neural population-level activity within the PFC is different for the match vs. non-match condition exclusively in the case of the feature-specific decision-making task. For this task, the non-match condition exhibited a greater firing rate and lower trial-to-trial variability in spike count compared to the feature-match condition. Furthermore, the feature-match condition exhibited lower variability compared to the spatial-match condition. This was accompanied by a faster behavioral response time for the feature-match compared to the spatial-match WM task. We attribute this lower across-trial spiking variability and behavioral response time to a higher task-relevant attentional level in the feature WM compared to the spatial WM task. The findings support our hypothesis for task-specific differences in the processing of feature vs. spatial WM within the PFC. This also confirms the general conclusion that PFC neurons play an important role during the process of matching-rule governed decision-making.
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Affiliation(s)
- Mohsen Parto Dezfouli
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. .,Neuroscience and Neuroengineering Research Laboratory, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
| | - Mohammad Zarei
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Christos Constantinidis
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Mohammad Reza Daliri
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. .,Neuroscience and Neuroengineering Research Laboratory, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
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26
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Krug K. Coding Perceptual Decisions: From Single Units to Emergent Signaling Properties in Cortical Circuits. Annu Rev Vis Sci 2020; 6:387-409. [PMID: 32600168 DOI: 10.1146/annurev-vision-030320-041223] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Spiking activity in single neurons of the primate visual cortex has been tightly linked to perceptual decisions. Any mechanism that reads out these perceptual signals to support behavior must respect the underlying neuroanatomy that shapes the functional properties of sensory neurons. Spatial distribution and timing of inputs to the next processing levels are critical, as conjoint activity of precursor neurons increases the spiking rate of downstream neurons and ultimately drives behavior. I set out how correlated activity might coalesce into a micropool of task-sensitive neurons signaling a particular percept to determine perceptual decision signals locally and for flexible interarea transmission depending on the task context. As data from more and more neurons and their complex interactions are analyzed, the space of computational mechanisms must be constrained based on what is plausible within neurobiological limits. This review outlines experiments to test the new perspectives offered by these extended methods.
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Affiliation(s)
- Kristine Krug
- Lehrstuhl für Sensorische Physiologie, Institut für Biologie, Otto-von-Guericke-Universität Magdeburg, 39120 Magdeburg, Germany; .,Leibniz-Institut für Neurobiologie, 39118 Magdeburg, Germany.,Department of Physiology, Anatomy, and Genetics, Oxford University, Oxford OX1 3PT, United Kingdom
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27
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Omer DB, Fekete T, Ulchin Y, Hildesheim R, Grinvald A. Dynamic Patterns of Spontaneous Ongoing Activity in the Visual Cortex of Anesthetized and Awake Monkeys are Different. Cereb Cortex 2020; 29:1291-1304. [PMID: 29718200 DOI: 10.1093/cercor/bhy099] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 04/12/2018] [Indexed: 11/14/2022] Open
Abstract
Ongoing internal cortical activity plays a major role in perception and behavior both in animals and humans. Previously we have shown that spontaneous patterns resembling orientation-maps appear over large cortical areas in the primary visual-cortex of anesthetized cats. However, it remains unknown 1) whether spontaneous-activity in the primate also displays similar patterns and 2) whether a significant difference exists between cortical ongoing-activity in the anesthetized and awake primate. We explored these questions by combining voltage-sensitive-dye imaging with multiunit and local-field-potential recordings. Spontaneously emerging orientation and ocular-dominance maps, spanning up to 6 × 6 mm2, were readily observed in anesthetized but not in awake monkeys. Nevertheless, spontaneous correlated-activity involving orientation-domains was observed in awake monkeys. Under both anesthetized and awake conditions, spontaneous correlated-activity coincided with traveling waves. We found that spontaneous activity resembling orientation-maps in awake animals spans smaller cortical areas in each instance, but over time it appears across all of V1. Furthermore, in the awake monkey, our results suggest that the synaptic strength had been completely reorganized including connections between dissimilar elements of the functional architecture. These findings lend support to the notion that ongoing-activity has many more fast switching representations playing an important role in cortical function and behavior.
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Affiliation(s)
- David B Omer
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Tomer Fekete
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Yigal Ulchin
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Rina Hildesheim
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Amiram Grinvald
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
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28
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Zhao Y, Yates JL, Levi AJ, Huk AC, Park IM. Stimulus-choice (mis)alignment in primate area MT. PLoS Comput Biol 2020; 16:e1007614. [PMID: 32421716 PMCID: PMC7259805 DOI: 10.1371/journal.pcbi.1007614] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/29/2020] [Accepted: 04/05/2020] [Indexed: 12/12/2022] Open
Abstract
For stimuli near perceptual threshold, the trial-by-trial activity of single neurons in many sensory areas is correlated with the animal's perceptual report. This phenomenon has often been attributed to feedforward readout of the neural activity by the downstream decision-making circuits. The interpretation of choice-correlated activity is quite ambiguous, but its meaning can be better understood in the light of population-wide correlations among sensory neurons. Using a statistical nonlinear dimensionality reduction technique on single-trial ensemble recordings from the middle temporal (MT) area during perceptual-decision-making, we extracted low-dimensional latent factors that captured the population-wide fluctuations. We dissected the particular contributions of sensory-driven versus choice-correlated activity in the low-dimensional population code. We found that the latent factors strongly encoded the direction of the stimulus in single dimension with a temporal signature similar to that of single MT neurons. If the downstream circuit were optimally utilizing this information, choice-correlated signals should be aligned with this stimulus encoding dimension. Surprisingly, we found that a large component of the choice information resides in the subspace orthogonal to the stimulus representation inconsistent with the optimal readout view. This misaligned choice information allows the feedforward sensory information to coexist with the decision-making process. The time course of these signals suggest that this misaligned contribution likely is feedback from the downstream areas. We hypothesize that this non-corrupting choice-correlated feedback might be related to learning or reinforcing sensory-motor relations in the sensory population.
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Affiliation(s)
- Yuan Zhao
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
| | - Jacob L. Yates
- Brain and Cognitive Science, University of Rochester, Rochester, New York, United States of America
| | - Aaron J. Levi
- Center for Perceptual Systems, Departments of Neuroscience & Psychology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Alexander C. Huk
- Center for Perceptual Systems, Departments of Neuroscience & Psychology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Il Memming Park
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
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29
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Levy M, Sporns O, MacLean JN. Network Analysis of Murine Cortical Dynamics Implicates Untuned Neurons in Visual Stimulus Coding. Cell Rep 2020; 31:107483. [PMID: 32294431 PMCID: PMC7218481 DOI: 10.1016/j.celrep.2020.03.047] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 01/22/2020] [Accepted: 03/13/2020] [Indexed: 02/02/2023] Open
Abstract
Unbiased and dense sampling of large populations of layer 2/3 pyramidal neurons in mouse primary visual cortex (V1) reveals two functional sub-populations: neurons tuned and untuned to drifting gratings. Whether functional interactions between these two groups contribute to the representation of visual stimuli is unclear. To examine these interactions, we summarize the population partial pairwise correlation structure as a directed and weighted graph. We find that tuned and untuned neurons have distinct topological properties, with untuned neurons occupying central positions in functional networks (FNs). Implementation of a decoder that utilizes the topology of these FNs yields accurate decoding of visual stimuli. We further show that decoding performance degrades comparably following manipulations of either tuned or untuned neurons. Our results demonstrate that untuned neurons are an integral component of V1 FNs and suggest that network interactions contain information about the stimulus that is accessible to downstream elements.
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Affiliation(s)
- Maayan Levy
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL 60637, USA
| | - Olaf Sporns
- Indiana University Network Science Institute, Indiana University, Bloomington, IN 47405, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Jason N MacLean
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL 60637, USA; Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA; Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior.
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30
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Gabitov E, Lungu O, Albouy G, Doyon J. Weaker Inter-hemispheric and Local Functional Connectivity of the Somatomotor Cortex During a Motor Skill Acquisition Is Associated With Better Learning. Front Neurol 2019; 10:1242. [PMID: 31827459 PMCID: PMC6890719 DOI: 10.3389/fneur.2019.01242] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 11/07/2019] [Indexed: 12/20/2022] Open
Abstract
Recently, an increasing interest in investigating interactions between brain regions using functional connectivity (FC) methods has shifted the initial focus of cognitive neuroimaging research from localizing functional circuits based on task activation to mapping brain networks based on intrinsic FC dynamics. Leveraging the advantages of the latter approach, it has been shown that despite primarily invariant intrinsic organization of the large-scale functional networks, interactions between and within these networks significantly differ between various behavioral and cognitive states. These differences presumably indicate transient reconfiguration of functional connections-an instantaneous process that flexibly mediates and calibrates human behavior according to momentary demands of the environment. Nevertheless, the specificity of these reconfigured FC patterns to the task at hand and their relevance to adaptive processes during learning remain elusive. To address this knowledge gap, we investigated (1) to what extent FC within the somatomotor network is reconfigured during motor skill practice, and (2) how these changes are related to learning. We applied a seed-driven FC approach to data collected during a continuous task-free condition, so-called resting state, and during a motor sequence learning task using functional magnetic resonance imaging. During the task, participants repeatedly performed a short five-element sequence with their non-dominant (left) hand. As predicted, such unimanual sequence production was associated with lateralized activation of the right somatomotor cortex (SMC). Using this "active" region as a seed, here we show that unimanual performance of the motor sequence relies on functional segregation between the two SMC and selective integration between the "active" SMC and supplementary motor area. Whereas, greater segregation between the two SMC was associated with gains in performance rate, greater segregation within the "active" SMC itself was associated with more consistent performance by the end of training. Nether the resting-state FC patterns within the somatomotor network nor their relative modulation by the task state predicted these behavioral benefits of learning. Our results suggest that task-induced FC changes reflect reconfiguration of the connectivity patterns within the somatomotor network rather than a simple amplification or silencing of its intrinsic dynamics. Such reconfiguration not only supports motor behavior but may also predict learning.
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Affiliation(s)
- Ella Gabitov
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Ovidiu Lungu
- Functional Neuroimaging Unit, Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC, Canada.,Département de Psychiatrie et d'Addictologie, Université de Montréal, Montreal, QC, Canada
| | - Geneviève Albouy
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Julien Doyon
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
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31
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Chen G, Gong P. Computing by modulating spontaneous cortical activity patterns as a mechanism of active visual processing. Nat Commun 2019; 10:4915. [PMID: 31664052 PMCID: PMC6820766 DOI: 10.1038/s41467-019-12918-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 10/07/2019] [Indexed: 01/23/2023] Open
Abstract
Cortical populations produce complex spatiotemporal activity spontaneously without sensory inputs. However, the fundamental computational roles of such spontaneous activity remain unclear. Here, we propose a new neural computation mechanism for understanding how spontaneous activity is actively involved in cortical processing: Computing by Modulating Spontaneous Activity (CMSA). Using biophysically plausible circuit models, we demonstrate that spontaneous activity patterns with dynamical properties, as found in empirical observations, are modulated or redistributed by external stimuli to give rise to neural responses. We find that this CMSA mechanism of generating neural responses provides profound computational advantages, such as actively speeding up cortical processing. We further reveal that the CMSA mechanism provides a unifying explanation for many experimental findings at both the single-neuron and circuit levels, and that CMSA in response to natural stimuli such as face images is the underlying neurophysiological mechanism of perceptual "bubbles" as found in psychophysical studies.
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Affiliation(s)
- Guozhang Chen
- School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia.,ARC Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales 2006, Australia
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia. .,ARC Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales 2006, Australia.
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32
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Ghanbari A, Lee CM, Read HL, Stevenson IH. Modeling stimulus-dependent variability improves decoding of population neural responses. J Neural Eng 2019; 16:066018. [PMID: 31404915 DOI: 10.1088/1741-2552/ab3a68] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. APPROACH In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models-the Conway-Maxwell-Poisson (CMP) model and negative binomial (NB) model-to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor > 1) or, for the CMP model, less than the mean (Fano factor < 1). MAIN RESULTS We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%-12% compared to the Poisson model. SIGNIFICANCE Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.
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Affiliation(s)
- Abed Ghanbari
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States of America
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33
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Dehaqani MRA, Vahabie AH, Parsa M, Noudoost B, Soltani A. Selective Changes in Noise Correlations Contribute to an Enhanced Representation of Saccadic Targets in Prefrontal Neuronal Ensembles. Cereb Cortex 2019; 28:3046-3063. [PMID: 29893800 PMCID: PMC6041979 DOI: 10.1093/cercor/bhy141] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 05/20/2018] [Indexed: 01/08/2023] Open
Abstract
An ensemble of neurons can provide a dynamic representation of external stimuli, ongoing processes, or upcoming actions. This dynamic representation could be achieved by changes in the activity of individual neurons and/or their interactions. To investigate these possibilities, we simultaneously recorded from ensembles of prefrontal neurons in non-human primates during a memory-guided saccade task. Using both decoding and encoding methods, we examined changes in the information content of individual neurons and that of ensembles between visual encoding and saccadic target selection. We found that individual neurons maintained their limited spatial sensitivity between these cognitive states, whereas the ensemble selectively improved its encoding of spatial locations far from the neurons’ preferred locations. This population-level “encoding expansion” was not due to the ceiling effect at the preferred locations and was accompanied by selective changes in noise correlations for non-preferred locations. Moreover, the encoding expansion was observed for ensembles of different types of neurons and could not be explained by shifts in the preferred location of individual neurons. Our results demonstrate that the representation of space by neuronal ensembles is dynamically enhanced prior to saccades, and this enhancement occurs alongside changes in noise correlations more than changes in the activity of individual neurons.
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Affiliation(s)
- Mohammad-Reza A Dehaqani
- Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Abdol-Hossein Vahabie
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | | | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City UT, USA
| | - Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover NH, USA
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34
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Bressloff PC, Carroll SR. Stochastic neural fields as gradient dynamical systems. Phys Rev E 2019; 100:012402. [PMID: 31499797 DOI: 10.1103/physreve.100.012402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Indexed: 11/07/2022]
Abstract
Continuous attractor neural networks are used extensively to model a variety of experimentally observed coherent brain states, ranging from cortical waves of activity to stationary activity bumps. The latter are thought to play an important role in various forms of neural information processing, including population coding in primary visual cortex (V1) and working memory in prefrontal cortex. However, one limitation of continuous attractor networks is that the location of the peak of an activity bump (or wave) can diffuse due to intrinsic network noise. This reflects marginal stability of bump solutions with respect to the action of an underlying continuous symmetry group. Previous studies have used perturbation theory to derive an approximate stochastic differential equation for the location of the peak (phase) of the bump. Although this method captures the diffusive wandering of a bump solution, it ignores fluctuations in the amplitude of the bump. In this paper, we show how amplitude fluctuations can be analyzed by reducing the underlying stochastic neural field equation to a finite-dimensional stochastic gradient dynamical system that tracks the stochastic motion of both the amplitude and phase of bump solutions. This allows us to derive exact expressions for the steady-state probability density and its moments, which are then used to investigate two major issues: (i) the input-dependent suppression of neural variability and (ii) noise-induced transitions to bump extinction. We develop the theory by considering the particular example of a ring attractor network with SO(2) symmetry, which is the most common architecture used in attractor models of working memory and population tuning in V1. However, we also extend the analysis to a higher-dimensional spherical attractor network with SO(3) symmetry which has previously been proposed as a model of orientation and spatial frequency tuning in V1. We thus establish how a combination of stochastic analysis and group theoretic methods provides a powerful tool for investigating the effects of noise in continuous attractor networks.
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Affiliation(s)
- Paul C Bressloff
- Department of Mathematics, University of Utah, Salt Lake City, Utah 84112, USA
| | - Samuel R Carroll
- Department of Mathematics, University of Utah, Salt Lake City, Utah 84112, USA
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35
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Coen-Cagli R, Solomon SS. Relating Divisive Normalization to Neuronal Response Variability. J Neurosci 2019; 39:7344-7356. [PMID: 31387914 PMCID: PMC6759019 DOI: 10.1523/jneurosci.0126-19.2019] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 06/13/2019] [Accepted: 06/18/2019] [Indexed: 01/13/2023] Open
Abstract
Cortical responses to repeated presentations of a sensory stimulus are variable. This variability is sensitive to several stimulus dimensions, suggesting that it may carry useful information beyond the average firing rate. Many experimental manipulations that affect response variability are also known to engage divisive normalization, a widespread operation that describes neuronal activity as the ratio of a numerator (representing the excitatory stimulus drive) and denominator (the normalization signal). Although it has been suggested that normalization affects response variability, we lack a quantitative framework to determine the relation between the two. Here we extend the standard normalization model, by treating the numerator and the normalization signal as variable quantities. The resulting model predicts a general stabilizing effect of normalization on neuronal responses, and allows us to infer the single-trial normalization strength, a quantity that cannot be measured directly. We test the model on neuronal responses to stimuli of varying contrast, recorded in primary visual cortex of male macaques. We find that neurons that are more strongly normalized fire more reliably, and response variability and pairwise noise correlations are reduced during trials in which normalization is inferred to be strong. Our results thus suggest a novel functional role for normalization, namely, modulating response variability. Our framework could enable a direct quantification of the impact of single-trial normalization strength on the accuracy of perceptual judgments, and can be readily applied to other sensory and nonsensory factors.SIGNIFICANCE STATEMENT Divisive normalization is a widespread neural operation across sensory and nonsensory brain areas, which describes neuronal responses as the ratio between the excitatory drive to the neuron and a normalization signal. Normalization plays a key role in several important computations, including adjusting the neuron's dynamic range, reducing redundancy, and facilitating probabilistic inference. However, the relation between normalization and neuronal response variability (a fundamental aspect of neural coding) remains unclear. Here we develop a new model and test it on primary visual cortex responses. We show that normalization has a stabilizing effect on neuronal activity, beyond the known suppression of firing rate. This modulation of variability suggests a new functional role for normalization in neural coding and perception.
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Affiliation(s)
- Ruben Coen-Cagli
- Department of Systems and Computational Biology, and
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461
| | - Selina S Solomon
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461
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36
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Andrei AR, Pojoga S, Janz R, Dragoi V. Integration of cortical population signals for visual perception. Nat Commun 2019; 10:3832. [PMID: 31444323 PMCID: PMC6707195 DOI: 10.1038/s41467-019-11736-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 07/31/2019] [Indexed: 11/09/2022] Open
Abstract
Visual stimuli evoke heterogeneous responses across nearby neural populations. These signals must be locally integrated to contribute to perception, but the principles underlying this process are unknown. Here, we exploit the systematic organization of orientation preference in macaque primary visual cortex (V1) and perform causal manipulations to examine the limits of signal integration. Optogenetic stimulation and visual stimuli are used to simultaneously drive two neural populations with overlapping receptive fields. We report that optogenetic stimulation raises firing rates uniformly across conditions, but improves the detection of visual stimuli only when activating cells that are preferentially-tuned to the visual stimulus. Further, we show that changes in correlated variability are exclusively present when the optogenetically and visually-activated populations are functionally-proximal, suggesting that correlation changes represent a hallmark of signal integration. Our results demonstrate that information from functionally-proximal neurons is pooled for perception, but functionally-distal signals remain independent.
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Affiliation(s)
- Ariana R Andrei
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX, 77030, USA
| | - Sorin Pojoga
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX, 77030, USA
| | - Roger Janz
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX, 77030, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX, 77030, USA.
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37
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Hennequin G, Ahmadian Y, Rubin DB, Lengyel M, Miller KD. The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability. Neuron 2019; 98:846-860.e5. [PMID: 29772203 PMCID: PMC5971207 DOI: 10.1016/j.neuron.2018.04.017] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 02/14/2018] [Accepted: 04/12/2018] [Indexed: 12/16/2022]
Abstract
Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states (“attractors”) or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic “stabilized supralinear network”), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception. A simple network model explains stimulus-tuning of cortical variability suppression Inhibition stabilizes recurrently interacting neurons with supralinear I/O functions Stimuli strengthen inhibitory stabilization around a stable state, quenching variability Single-trial V1 data are compatible with this model and rules out competing proposals
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Affiliation(s)
- Guillaume Hennequin
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.
| | - Yashar Ahmadian
- Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Centre de Neurophysique, Physiologie, et Pathologie, CNRS, 75270 Paris Cedex 06, France; Institute of Neuroscience, Department of Biology and Mathematics, University of Oregon, Eugene, OR 97403, USA
| | - Daniel B Rubin
- Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Neurology, Massachusetts General Hospital and Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK; Department of Cognitive Science, Central European University, 1051 Budapest, Hungary
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
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38
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Parto Dezfouli M, Zarei M, Jahed M, Daliri MR. Stimulus-Specific Adaptation Decreases the Coupling of Spikes to LFP Phase. Front Neural Circuits 2019; 13:44. [PMID: 31333419 PMCID: PMC6616079 DOI: 10.3389/fncir.2019.00044] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Accepted: 06/18/2019] [Indexed: 11/19/2022] Open
Abstract
Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation (SSA) has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of SSA on the spike–LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms was evaluated. Here, we employed phase locking value method for calculating the SPC. Our data show a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. As a result, the preferred phase of LFP may play a key role in coordinating neuronal spiking activity in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.
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Affiliation(s)
- Mohsen Parto Dezfouli
- Neuroscience and Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Zarei
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mehran Jahed
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mohammad Reza Daliri
- Neuroscience and Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
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39
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Ocker GK, Doiron B. Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing Plasticity. Cereb Cortex 2019; 29:937-951. [PMID: 29415191 PMCID: PMC7963120 DOI: 10.1093/cercor/bhy001] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 01/01/2018] [Accepted: 01/05/2018] [Indexed: 12/15/2022] Open
Abstract
The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectivity is actively reinforced by spike train correlations during the spontaneous dynamics. Furthermore, the stimulus coding by cell assemblies is actively maintained by these internally generated spiking correlations, suggesting a new role for noise correlations in neural coding. Assembly formation has often been associated with firing rate-based plasticity schemes; our theory provides an alternative and complementary framework, where fine temporal correlations and STDP form and actively maintain learned structure in cortical networks.
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Affiliation(s)
- Gabriel Koch Ocker
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brent Doiron
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
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40
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Bressloff PC. Stochastic neural field model of stimulus-dependent variability in cortical neurons. PLoS Comput Biol 2019; 15:e1006755. [PMID: 30883546 PMCID: PMC6438587 DOI: 10.1371/journal.pcbi.1006755] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 03/28/2019] [Accepted: 02/26/2019] [Indexed: 01/03/2023] Open
Abstract
We use stochastic neural field theory to analyze the stimulus-dependent tuning of neural variability in ring attractor networks. We apply perturbation methods to show how the neural field equations can be reduced to a pair of stochastic nonlinear phase equations describing the stochastic wandering of spontaneously formed tuning curves or bump solutions. These equations are analyzed using a modified version of the bivariate von Mises distribution, which is well-known in the theory of circular statistics. We first consider a single ring network and derive a simple mathematical expression that accounts for the experimentally observed bimodal (or M-shaped) tuning of neural variability. We then explore the effects of inter-network coupling on stimulus-dependent variability in a pair of ring networks. These could represent populations of cells in two different layers of a cortical hypercolumn linked via vertical synaptic connections, or two different cortical hypercolumns linked by horizontal patchy connections within the same layer. We find that neural variability can be suppressed or facilitated, depending on whether the inter-network coupling is excitatory or inhibitory, and on the relative strengths and biases of the external stimuli to the two networks. These results are consistent with the general observation that increasing the mean firing rate via external stimuli or modulating drives tends to reduce neural variability.
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Affiliation(s)
- Paul C. Bressloff
- Department of Mathematics, University of Utah, Salt Lake City, Utah, USA
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41
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Bányai M, Lazar A, Klein L, Klon-Lipok J, Stippinger M, Singer W, Orbán G. Stimulus complexity shapes response correlations in primary visual cortex. Proc Natl Acad Sci U S A 2019; 116:2723-2732. [PMID: 30692266 PMCID: PMC6377442 DOI: 10.1073/pnas.1816766116] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure, and arise from structured internal dynamics. However, most theories of visual perception treat contributions of neurons to the representation of stimuli independently and focus on mean responses. Here, we argue that, in a functional model of visual perception, featuring probabilistic inference over a hierarchy of features, inferences about high-level features modulate inferences about low-level features ultimately introducing structured internal dynamics and patterns in SCCs. Specifically, high-level inferences for complex stimuli establish the local context in which neurons in the primary visual cortex (V1) interpret stimuli. Since the local context differentially affects multiple neurons, this conjecture predicts specific modulations in the fine structure of SCCs as stimulus identity and, more importantly, stimulus complexity varies. We designed experiments with natural and synthetic stimuli to measure the fine structure of SCCs in V1 of awake behaving macaques and assessed their dependence on stimulus identity and stimulus statistics. We show that the fine structure of SCCs is specific to the identity of natural stimuli and changes in SCCs are independent of changes in response mean. Critically, we demonstrate that stimulus specificity of SCCs in V1 can be directly manipulated by altering the amount of high-order structure in synthetic stimuli. Finally, we show that simple phenomenological models of V1 activity cannot account for the observed SCC patterns and conclude that the stimulus dependence of SCCs is a natural consequence of structured internal dynamics in a hierarchical probabilistic model of natural images.
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Affiliation(s)
- Mihály Bányai
- Computational Systems Neuroscience Lab, MTA Wigner Research Centre for Physics, 1121 Budapest, Hungary;
| | - Andreea Lazar
- Ernst Strüngmann Institute for Neuroscience, 60528 Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
- Max Planck Institute for Brain Research, 60438 Frankfurt am Main, Germany
| | - Liane Klein
- Ernst Strüngmann Institute for Neuroscience, 60528 Frankfurt am Main, Germany
- Max Planck Institute for Brain Research, 60438 Frankfurt am Main, Germany
- International Max Planck Research School for Neural Circuits, 60438 Frankfurt am Main, Germany
| | - Johanna Klon-Lipok
- Ernst Strüngmann Institute for Neuroscience, 60528 Frankfurt am Main, Germany
- Max Planck Institute for Brain Research, 60438 Frankfurt am Main, Germany
| | - Marcell Stippinger
- Computational Systems Neuroscience Lab, MTA Wigner Research Centre for Physics, 1121 Budapest, Hungary
| | - Wolf Singer
- Ernst Strüngmann Institute for Neuroscience, 60528 Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
- Max Planck Institute for Brain Research, 60438 Frankfurt am Main, Germany
| | - Gergő Orbán
- Computational Systems Neuroscience Lab, MTA Wigner Research Centre for Physics, 1121 Budapest, Hungary
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42
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Huang C, Ruff DA, Pyle R, Rosenbaum R, Cohen MR, Doiron B. Circuit Models of Low-Dimensional Shared Variability in Cortical Networks. Neuron 2018; 101:337-348.e4. [PMID: 30581012 DOI: 10.1016/j.neuron.2018.11.034] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 10/25/2018] [Accepted: 11/19/2018] [Indexed: 12/19/2022]
Abstract
Trial-to-trial variability is a reflection of the circuitry and cellular physiology that make up a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional. Previous model cortical networks cannot explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, networks of model spiking neurons internally generate low-dimensional shared variability that captures population activity recorded in vivo. Shifting spatial attention into the receptive field of visual neurons has been shown to differentially modulate shared variability within and between brain areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation. Our work provides a critical link between observed cortical circuit structure and realistic shared neuronal variability and its modulation.
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Affiliation(s)
- Chengcheng Huang
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Douglas A Ruff
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ryan Pyle
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN, USA
| | - Marlene R Cohen
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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43
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Concurrent influence of top-down and bottom-up inputs on correlated activity of Macaque extrastriate neurons. Nat Commun 2018; 9:5393. [PMID: 30568166 PMCID: PMC6300596 DOI: 10.1038/s41467-018-07816-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 11/23/2018] [Indexed: 11/09/2022] Open
Abstract
Correlations between neurons can profoundly impact the information encoding capacity of a neural population. We studied how maintenance of visuospatial information affects correlated activity in visual areas by recording the activity of neurons in visual area MT of rhesus macaques during a spatial working memory task. Correlations between MT neurons depended upon the spatial overlap between neurons’ receptive fields. These correlations were influenced by the content of working memory, but the effect of a top-down memory signal differed in the presence or absence of bottom-up visual input. Neurons representing the same area of space showed increased correlations when remembering a location in their receptive fields in the absence of visual input, but decreased correlations in the presence of a visual stimulus. This set of results reveals the correlating nature of top-down signals influencing visual areas and uncovers how such a correlating signal, in interaction with bottom-up information, could enhance sensory representations. Changes in correlated activity of neurons are believed to influence their information coding capacity. Here, the authors show how top-down and bottom-up inputs and their interaction differentially alter the correlated activity of neurons in extrastriate cortex
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44
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Hebart MN, Baker CI. Deconstructing multivariate decoding for the study of brain function. Neuroimage 2018; 180:4-18. [PMID: 28782682 PMCID: PMC5797513 DOI: 10.1016/j.neuroimage.2017.08.005] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 07/28/2017] [Accepted: 08/01/2017] [Indexed: 12/24/2022] Open
Abstract
Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function.
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Affiliation(s)
- Martin N Hebart
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Chris I Baker
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
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45
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Lombardo JA, Macellaio MV, Liu B, Palmer SE, Osborne LC. State dependence of stimulus-induced variability tuning in macaque MT. PLoS Comput Biol 2018; 14:e1006527. [PMID: 30312315 PMCID: PMC6211771 DOI: 10.1371/journal.pcbi.1006527] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 11/01/2018] [Accepted: 09/25/2018] [Indexed: 12/31/2022] Open
Abstract
Behavioral states marked by varying levels of arousal and attention modulate some properties of cortical responses (e.g. average firing rates or pairwise correlations), yet it is not fully understood what drives these response changes and how they might affect downstream stimulus decoding. Here we show that changes in state modulate the tuning of response variance-to-mean ratios (Fano factors) in a fashion that is neither predicted by a Poisson spiking model nor changes in the mean firing rate, with a substantial effect on stimulus discriminability. We recorded motion-sensitive neurons in middle temporal cortex (MT) in two states: alert fixation and light, opioid anesthesia. Anesthesia tended to lower average spike counts, without decreasing trial-to-trial variability compared to the alert state. Under anesthesia, within-trial fluctuations in excitability were correlated over longer time scales compared to the alert state, creating supra-Poisson Fano factors. In contrast, alert-state MT neurons have higher mean firing rates and largely sub-Poisson variability that is stimulus-dependent and cannot be explained by firing rate differences alone. The absence of such stimulus-induced variability tuning in the anesthetized state suggests different sources of variability between states. A simple model explains state-dependent shifts in the distribution of observed Fano factors via a suppression in the variance of gain fluctuations in the alert state. A population model with stimulus-induced variability tuning and behaviorally constrained information-limiting correlations explores the potential enhancement in stimulus discriminability by the cortical population in the alert state. The brain controls behavior fluidly in a wide variety of cognitive contexts that alter the precision of neural responses. We examine how neural variability changes versus the mean response as a function of the stimulus and the behavioral state. We show that this scaled variability can have qualitatively different stimulus tuning in different behavioral contexts. In alert primates, scaled variability is tuned to the direction of motion of a visual stimulus and decreases around the preferred direction of each neuron. Under anesthesia, neurons show flat scaled variability tuning and, overall, responses are significantly more variable. We develop a simple model that includes a parameter describing firing rate gain fluctuations that can explain these changes. Our results suggest that tuned decreases in scaled variability during wakefulness may be mediated by an active process that suppresses synchronization and makes information transmission more reliable.
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Affiliation(s)
- Joseph A. Lombardo
- Computational Neuroscience Graduate Program, University of Chicago, Chicago, Illinois, United States of America
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
| | - Matthew V. Macellaio
- Neurobiology Graduate Program, University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
| | - Bing Liu
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
| | - Stephanie E. Palmer
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
- Department of Physics, University of Chicago, Chicago, Illinois, United States of America
- * E-mail: (SEP); (LCO)
| | - Leslie C. Osborne
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
- * E-mail: (SEP); (LCO)
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46
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Lynch LK, Lu KH, Wen H, Zhang Y, Saykin AJ, Liu Z. Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions. Hum Brain Mapp 2018; 39:4939-4948. [PMID: 30144210 DOI: 10.1002/hbm.24335] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 07/12/2018] [Accepted: 07/16/2018] [Indexed: 11/11/2022] Open
Abstract
During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task-evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task-evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task-evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non-visual sensory or motor cortices. Our results suggest that task-evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter-regional correlation.
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Affiliation(s)
- Lauren K Lynch
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana
| | - Kun-Han Lu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Haiguang Wen
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Yizhen Zhang
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, Indiana
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
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47
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Riehle A, Brochier T, Nawrot M, Grün S. Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex. Front Neural Circuits 2018; 12:52. [PMID: 30050415 PMCID: PMC6052126 DOI: 10.3389/fncir.2018.00052] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 06/15/2018] [Indexed: 11/13/2022] Open
Abstract
Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependance. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that the FF significantly decreases from wait to movement, while the CV2 increases. The more regular firing (expressed by a low CV2) during wait is related to an increased power of local field potential (LFP) beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF ≈ CV2. This expectation was met during movement, but not during wait where FF ≫ CV22. Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.
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Affiliation(s)
- Alexa Riehle
- UMR7289 Institut de Neurosciences de la Timone (INT), Centre National de la Recherche Scientifique (CNRS)-Aix-Marseille Université (AMU), Marseille, France.,Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Brain Institute I, Forschungszentrum Jülich, Jülich, Germany
| | - Thomas Brochier
- UMR7289 Institut de Neurosciences de la Timone (INT), Centre National de la Recherche Scientifique (CNRS)-Aix-Marseille Université (AMU), Marseille, France
| | - Martin Nawrot
- Computational Systems Neuroscience, Institute for Zoology, University of Cologne, Cologne, Germany
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Brain Institute I, Forschungszentrum Jülich, Jülich, Germany.,RIKEN Brain Science Institute (BSI), Wako, Japan.,Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
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48
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Bolt T, Anderson ML, Uddin LQ. Beyond the evoked/intrinsic neural process dichotomy. Netw Neurosci 2018; 2:1-22. [PMID: 29911670 PMCID: PMC5989985 DOI: 10.1162/netn_a_00028] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Accepted: 09/28/2017] [Indexed: 01/20/2023] Open
Abstract
Contemporary functional neuroimaging research has increasingly focused on characterization of intrinsic or "spontaneous" brain activity. Analysis of intrinsic activity is often contrasted with analysis of task-evoked activity that has traditionally been the focus of cognitive neuroscience. But does this evoked/intrinsic dichotomy adequately characterize human brain function? Based on empirical data demonstrating a close functional interdependence between intrinsic and task-evoked activity, we argue that the dichotomy between intrinsic and task-evoked activity as unobserved contributions to brain activity is artificial. We present an alternative picture of brain function in which the brain's spatiotemporal dynamics do not consist of separable intrinsic and task-evoked components, but reflect the enaction of a system of mutual constraints to move the brain into and out of task-appropriate functional configurations. According to this alternative picture, cognitive neuroscientists are tasked with describing both the temporal trajectory of brain activity patterns across time, and the modulation of this trajectory by task states, without separating this process into intrinsic and task-evoked components. We argue that this alternative picture of brain function is best captured in a novel explanatory framework called enabling constraint. Overall, these insights call for a reconceptualization of functional brain activity, and should drive future methodological and empirical efforts.
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Affiliation(s)
- Taylor Bolt
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Michael L. Anderson
- Department of Philosophy and Brain and Mind Institute, Western University, London, ON, Canada
- Institute for Advanced Computer Studies, Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Lucina Q. Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA
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49
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Pernice V, da Silveira RA. Interpretation of correlated neural variability from models of feed-forward and recurrent circuits. PLoS Comput Biol 2018; 14:e1005979. [PMID: 29408930 PMCID: PMC5833435 DOI: 10.1371/journal.pcbi.1005979] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 03/01/2018] [Accepted: 01/10/2018] [Indexed: 11/18/2022] Open
Abstract
Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing. The response of neurons to a stimulus is variable across trials. A natural solution for reliable coding in the face of noise is the averaging across a neural population. The nature of this averaging depends on the structure of noise correlations in the neural population. In turn, the correlation structure depends on the way noise and correlations are generated in neural circuits. It is in general difficult to identify the origin of correlations from the observed population activity alone. In this article, we explore different theoretical scenarios of the way in which correlations can be generated, and we relate these to the architecture of feed-forward and recurrent neural circuits. Analyzing population recordings of the activity in mouse auditory cortex in response to sound stimuli, we find that population statistics are consistent with those generated in a recurrent network model. Using this model, we can then quantify the effects of network properties on average population responses, noise correlations, and the representation of sensory information.
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Affiliation(s)
- Volker Pernice
- Department of Physics, Ecole Normale Supérieure, Paris, France
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité, Sorbonne Universités UPMC Univ Paris 06; CNRS, Paris, France
| | - Rava Azeredo da Silveira
- Department of Physics, Ecole Normale Supérieure, Paris, France
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité, Sorbonne Universités UPMC Univ Paris 06; CNRS, Paris, France
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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50
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Franović I, Klinshov V. Clustering promotes switching dynamics in networks of noisy neurons. CHAOS (WOODBURY, N.Y.) 2018; 28:023111. [PMID: 29495663 DOI: 10.1063/1.5017822] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.
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Affiliation(s)
- Igor Franović
- Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia
| | - Vladimir Klinshov
- Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanov Street, 603950 Nizhny Novgorod, Russia
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