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Pairwise Synchrony and Correlations Depend on the Structure of the population code in Visual Cortex

CELL REPORTS 2020年第6期33卷 108367-108367页

作者： Koren, Veronika Andrei, Ariana R. Hu, Ming Dragoi, Valentin Obermayer, Klaus Tech Univ Berlin Inst Software Engn & Theoret Comp Sci D-10587 Berlin Germany Bernstein Ctr Computat Neurosci Berlin Germany Univ Texas Houston Dept Neurobiol & Anat Sch Med Houston TX 77030 USA Tech Univ Berlin Inst Math D-10623 Berlin Germany Baylor Coll Med Houston TX 77030 USA

In visual areas of primates, neurons activate in parallel while the animal is engaged in a behavioral task. In this study, we examine the structure of the population code while the animal performs delayed match-to-sample tasks on complex natural images. The macaque monkeys visualized two consecutive stimuli that were either the same or different, while being recorded with laminar arrays across the cortical depth in cortical areas V1 and V4. We decode correct choice behavior from neural populations of simultaneously recorded units. Utilizing decoding weights, we divide neurons into most informative and less informative and show that most informative neurons in V4, but not in V1, are more strongly synchronized, coupled, and correlated than less informative neurons. Because neurons are divided into two coding pools according to their coding preference, in V4, but not in V1, spiking synchrony, coupling, and correlations within the coding pool are stronger than across coding pools.

关键词： monkey visual cortex discrimination choice behavior classification noise correlations neural coding population code information

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Reading a population code: a multi-scale neural model for representing binocular disparity

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VISION RESEARCH 2003年第4期43卷 445-466页

作者： Tsai, JJ Victor, JD Cornell Univ Weill Med Coll Dept Neurol & Neurosci New York NY 10021 USA

Although binocular neurons in the primary visual cortex are sensitive to retinal disparity, their activity does not constitute an unambiguous disparity signal. A multi-spatial-scale neural model for disparity computation is developed to examine how population activity might be interpreted to overcome ambiguities at the single neuron level. The model incorporates a front end that encodes disparity by a family of complex cell-like energy units and a second stage that reads the population activity. Disparity is recovered by matching the population response to a set of canonical templates, derived from the mean response to white noise stimuli at a range of disparities. Model predictions are qualitatively consistent with a variety of psychophysical results in the literature, including the effects of spatial frequency on stereoacuity and bias in perceived depths, and the effect of standing disparity on increment thresholds. Model predictions are also consistent with data on qualitative appearance of complex stimuli, including depth averaging, transparency, and corrugation. The model also accounts for the non-linear interaction of disparities in compound grating stimuli. These results show that a template-match approach reduces ambiguities in individual and pooled neuronal responses, and allows for a broader range of percepts, consistent with psychophysics, than other models. Thus, the pattern of neural population activity across spatial scales is a better candidate for the neural correlate of depth perception than the activity of single neurons or the pooled activity of multiple neurons. (C) 2003 Elsevier Science Ltd. All rights reserved.

关键词： stereo vision population code spatial frequency pooling intersection-of-constraints disparity interaction depth transparency threshold

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Encoding innately recognized odors via a generalized population code

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CURRENT BIOLOGY 2021年第9期31卷 1813-+页

作者： Qiu, Qiang Wu, Yunming Ma, Limei Yu, C. Ron Stowers Inst Med Res 1000 East 50th St Kansas City MO 64110 USA Univ Kansas Med Ctr Dept Anat & Cell Biol 3901 Rainbow Blvd Kansas City KS 66160 USA Twitter YuLab19 Kansas City KS 66160 USA

Odors carrying intrinsic values often trigger instinctive aversive or attractive responses. It is not known how innate valence is encoded. An intuitive model suggests that the information is conveyed through specific channels in hardwired circuits along the olfactory pathway, insulated from influences of other odors, to trigger innate responses. Here, we show that in mice, mixing innately aversive or attractive odors with a neutral odor and, surprisingly, mixing two odors with the same valence, abolish the innate behavioral responses. Recordings from the olfactory bulb indicate that odors are not masked at the level of peripheral activation and glomeruli independently encode components in the mixture. In contrast, crosstalk among the mitral and tufted (M/T) cells changes their patterns of activity such that those elicited by the mixtures can no longer be linearly decoded as separate components. The changes in behavioral and M/T cell responses are associated with reduced activation of brain areas linked to odor preferences. Thus, crosstalk among odor channels at the earliest processing stage in the olfactory pathway leads to re-coding of odor identity to abolish valence associated with the odors. These results are inconsistent with insulated labeled lines and support a model of a common mechanism of odor recognition for both innate and learned valence associations.

关键词： olfactory odor mixture configural perception elemental perception population code odor valence perceptual segmentation

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population codes in the visual cortex

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NEUROSCIENCE RESEARCH 2013年第3期76卷 101-105页

作者： Tanabe, Seiji Yeshiva Univ Albert Einstein Coll Med Dept Neurosci Bronx NY 10461 USA

Every sensory event elicits activity in a broad population of cells that is distributed within and across cortical areas. How these neurons function together to represent the sensory environment is a major question in systems neuroscience. A number of proposals have been made, and recent advances in multi-neuronal recording have begun to allow researchers to test the predictions of these population-coding theories. In this review, I provide an introduction to some of the key concepts in population coding and describe several studies in the recent literature. The focus of this review is on sensory representation in the visual cortex and related perceptual decisions. The frameworks used to study population coding include population vectors, linear decoders, and Bayesian inference. Simple examples are provided to illustrate these concepts. Testing theories of population coding is an emerging subject in systems neuroscience, but advances in multi-neuronal recording and analysis suggest that an understanding is within reach. (c) 2013 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

关键词： population code Visual cortex Perceptual decision Linear decoder Bayesian inference

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Coordinate-free sensorimotor processing: computing with population codes

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NEURAL NETWORKS 1998年第7-8期11卷 1417-1428页

作者： Morasso, PG Sanguineti, V Frisone, F Perico, L Univ Genoa DIST I-16145 Genoa Italy NW Univ Sch Med Dept Physiol Chicago IL USA

The purpose of the study is to outline a computational architecture for the intelligent processing of sensorimotor patterns. The focus is on the nature of the internal representations of the outside world which are necessary for planning and other goal-oriented functions. A model of cortical map dynamics and self-organization is proposed that integrates a number of concepts and methods partly explored in the field. The novelty and the biological plausibility is related to the global architecture which allows one to deal with sensorimotor patterns in a coordinate-free way, using population codes as distributed internal representations of external variables and the coupled dynamics of cortical maps as a general tool of trajectory formation. The basic computational features of the model are demonstrated in the case of articulatory speech synthesis and some of the metric properties are evaluated by means of simple simulation studies. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： population code cortical map cortical dynamics field computing self-organization speech topology representing network Hebbian learning

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Inference in the Brain: Statistics Flowing in Redundant population codes

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NEURON 2017年第5期94卷 943-953页

作者： Pitkow, Xaq Angelaki, Dora E. Baylor Coll Med Dept Neurosci Houston TX 77030 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors.

关键词： brain inference theory population code message-passing redundant coding nuisance nonlinear

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Computational model of layer 2/3 in mouse primary visual cortex explains observed visuomotor mismatch response

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2024年第4期52卷 323-329页

作者： Hoffmann, Heiko Magimine LLC POB 941154 Simi Valley CA 93094 USA

Activity in layer 2/3 of the mouse primary visual cortex has been shown to depend both on visual input and the mouse's locomotion. Moreover, this activity is altered by a mismatch between the observed visual flow and the predicted visual flow from locomotion. Here, I present a simple computational model that explains previously reported recordings from layer 2/3 neurons in mice. In my model, layer 2/3 encodes the velocity difference between the estimate from visual flow and the prediction from locomotion using a neural population code. Moreover, I describe a hypothesized mechanism for how the brain may carry out computations of variables encoded in population codes. This mechanism may point to a general principle for computing any mathematical function in the brain.

关键词： Primary visual cortex population code Visuomotor Prediction error

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Electric Stimulation Elicits Heterogeneous Responses in ON but Not OFF Retinal Ganglion Cells to Transmit Rich Neural Information

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 300-309页

作者： Kang, Joon Ho Jang, Ye Ji Kim, Taegon Lee, Byung Chul Lee, Soo Hyun Im, Maesoon Korea Inst Sci & Technol KIST Brain Sci Inst Seoul 02792 South Korea Seoul Natl Univ Sci & Technol Dept Elect & IT Media Engn Seoul 01811 South Korea Univ Sci & Technol UST KIST Sch Div Biomed Sci & Technol Seoul 02792 South Korea Harvard Med Sch Massachusetts Gen Hosp Dept Neurosurg Boston MA 02114 USA

Retinal implants electrically stimulate surviving retinal neurons to restore vision in people blinded by outer retinal degeneration. Although the healthy retina is known to transmit a vast amount of visual information to the brain, it has not been studied whether prosthetic vision contains a similar amount of information. Here, we assessed the neural information transmitted by population responses arising in brisk transient (BT) and brisk sustained (BS) subtypes of ON and OFF retinal ganglion cells (RGCs) in the rabbit retina. To correlate the response heterogeneity and the information transmission, we first quantified the cell-to-cell heterogeneity by calculating the spike time tiling coefficient (STTC) across spiking patterns of RGCs in each type. Then, we computed the neural information encoded by the RGC population in a given type. In responses to light stimulation, spiking activities were more heterogeneous in OFF than ON RGCs (STTCAVG = 0.36, 0.45, 0.77 and 0.55 for OFF BT, OFF BS, ON BT, and ON BS, respectively). Interestingly, however, in responses to electric stimulation, both BT and BS subtypes of OFF RGCs showed remarkably homogeneous spiking patterns across cells (STTCAVG = 0.93 and 0.82 for BT and BS, respectively), whereas the two subtypes of ON RGCs showed slightly increased populational heterogeneity compared to light-evoked responses (STTCAVG = 0.71 and 0.63 for BT and BS, respectively). Consequently, the neural information encoded by the electrically-evoked responses of a population of 15 RGCs was substantially lower in the OFF than the ON pathway: OFF BT and BS cells transmit only similar to 23% and similar to 53% of the neural information transmitted by their ON counterparts. Together with previously-reported natural spiking activities in ON RGCs, the higher neural information may make ON responses more recognizable, eliciting the biased percepts of bright phosphenes.

关键词： Retinal implant heterogeneity population code spike time tiling coefficient (STTC) neural information

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The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unirnodal or monotonic tuning curves

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2015年第JAN期9卷 18页

作者： Yarrow, Stuart Series, Peggy Univ Edinburgh Sch Informat Inst Adapt & Neural Computat Edinburgh EH8 9AB Midlothian Scotland

Tuning curves and receptive fields are widely used to describe the selectivity of sensory neurons, but the relationship between firing rates and information is not always intuitive. Neither high firing rates nor high tuning curve gradients necessarily mean that stimuli at that part of the tuning curve are well represented by a neuron. Recent research has shown that trial-to-trial variability (noise) and population size can strongly affect which stimuli are most precisely represented by a neuron in the context of a population code (the best-encoded stimulus), and that different measures of information can give conflicting indications. Specifically, the Fisher information is greatest where the tuning curve gradient is greatest, such as on the flanks of peaked tuning curves, but the stimulus-specific information (SSI) is greatest at the tuning curve peak for small populations with high trial-to-trial variability. Previous research in this area has focussed upon unimodal (peaked) tuning curves, and in this article we extend these analyses to monotonic tuning curves. In addition, we examine how stimulus spacing in forced choice tasks affects the best-encoded stimulus. Our results show that, regardless of the tuning curve, Fisher information correctly predicts the best-encoded stimulus for large populations and where the stimuli are closely spaced in forced choice tasks. In smaller populations with high variability, or in forced choice tasks with widely-spaced choices, the best-encoded stimulus falls at the peak of unimodal tuning curves, but is more variable for monotonic tuning curves. Task, population size and variability all need to be considered when assessing which stimuli a neuron represents, but the best-encoded stimulus can be estimated on a case-by case basis using commonly available computing facilities.

关键词： population code SSI fisher information Chernoff tuning curve monotonic unimodal

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Fisher information and optimal odor sensors

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NEUROCOMPUTING 2001年 38卷 335-341页

作者： Sánchez-Montañes, MA Pearce, TC Univ Autonoma Madrid ETS Informat E-28049 Madrid Spain Univ Leicester Dept Engn Leicester LE1 7RH Leics England

We discuss how the Fisher information matrix (FIM) may be used as part of an optimization procedure for selecting odor sensors within a population so as to maximize the accuracy with which the overall sensory system may estimate the stimulus. While the same approach may be equally applied to any sensory system that exploits a population coding of the stimulus in order to optimize its performance, we demonstrate how this technique may be used to analyze the performance of both biological and artificial olfactory systems. Thus one application of this method is the optimal design of arrays of artificial olfactory sensors. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： optimization of sensory arrays noise Fisher information chemical sensors population code

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