Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics

Sokoloski, Sacha

doi:10.1162/neco_a_00991

Cited by 8 publications

(7 citation statements)

References 63 publications

(128 reference statements)

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“…Our framework is particularly relevant for probabilistic theories of neural coding based on the theory of exponential families ( Beck et al, 2007 ), which include theories that address the linearity of Bayesian inference in neural circuits ( Ma et al, 2006 ), the role of phenomena such as divisive normalization in neural computation ( Beck et al, 2011a ), Bayesian inference about dynamic stimuli ( Makin et al, 2015 ; Sokoloski, 2017 ), and the metabolic efficiency of neural coding ( Ganguli and Simoncelli, 2014 ; Yerxa et al, 2020 ). These theories have proven difficult to validate quantitatively with neural data due to a lack of statistical models which are both compatible with their exponential family formulation, and can model correlated activity in recordings of large neural populations.…”

Section: Resultsmentioning

confidence: 99%

Modelling the neural code in large populations of correlated neurons

Sokoloski

Aschner

Coen-Cagli

2020

Preprint

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The activity of a neural population encodes information about the stimulus that caused it, and decoding population activity reveals how neural circuits process that information. Correlations between neurons strongly impact both encoding and decoding, yet we still lack models that simultaneously capture stimulus encoding by large populations of correlated neurons and allow for accurate decoding of stimulus information, thus limiting our quantitative understanding of the neural code. To address this, we propose a class of models of large-scale population activity based on the theory of exponential family distributions. We apply our models to macaque primary visual cortex (V1) recordings, and show they capture a wide range of response statistics, facilitate accurate Bayesian decoding, and provide interpretable representations of fundamental properties of the neural code. Ultimately, our framework could allow researchers to quantitatively validate predictions of theories of neural coding against both large-scale response recordings and cognitive performance.

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Section: Resultsmentioning

confidence: 99%

Modelling the neural code in large populations of correlated neurons

Sokoloski

Aschner

Coen-Cagli

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our framework is particularly relevant for probabilistic theories of neural coding based on the theory of exponential families ( Beck et al, 2007 ), which include theories that address the linearity of Bayesian inference in neural circuits ( Ma et al, 2006 ), the role of phenomena such as divisive normalization in neural computation ( Beck et al, 2011a ), Bayesian inference about dynamic stimuli ( Makin et al, 2015 ; Sokoloski, 2017 ), and the metabolic efficiency of neural coding ( Ganguli and Simoncelli, 2014 ; Yerxa et al, 2020 ). These theories have proven difficult to validate quantitatively with neural data due to a lack of statistical models which are both compatible with their exponential family formulation (see Equation 4 ), and can model correlated activity in recordings of large neural populations.…”

Section: Discussionmentioning

confidence: 99%

Modelling the neural code in large populations of correlated neurons

Sokoloski

Aschner

Coen-Cagli

2021

eLife

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Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.

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“…Models of neural online inference usually seek to obtain the marginal p(z t |x 1:t ) [11,42] or, in addition, the pairwise joint p(z t-1 , z t |x 1:t ) [28]. However, postdiction requires updating all the latent variables z 1:t given each new observation x t .…”

Section: Dynamical Encoding Functionsmentioning

confidence: 99%

A neurally plausible model for online recognition and postdiction in a dynamical environment

Wenliang

Sahani

2019

Preprint

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Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts, which are informed both by sensory evidence and by prior experience about the causal structure of the environment. It is hypothesized that the brain establishes these structures using an internal model of how the observed patterns can be generated from relevant but unobserved causes. In dynamic environments, such integration often takes the form of postdiction, wherein later sensory evidence affects inferences about earlier percepts. As the brain must operate in current time, without the luxury of acausal propagation of information, how does such postdictive inference come about? Here, we propose a general framework for neural probabilistic inference in dynamic models based on the distributed distributional code (DDC) representation of uncertainty, naturally extending the underlying encoding to incorporate implicit probabilistic beliefs about both present and past. We show that, as in other uses of the DDC, an inferential model can be learned efficiently using samples from an internal model of the world. Applied to stimuli used in the context of psychophysics experiments, the framework provides an online and plausible mechanism for inference, including postdictive effects.Behavioral [3,5,23,30,50] and physiological [8,9,15] findings suggest that the brain acquires an internal model of how relevant states of the world evolve in time, and how they give rise to the stream of sensory evidence. Recognition is then formally a process of statistical inference to form perceptual beliefs about the trajectory of latent causes given observations in time. While this type of 33rd

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Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics

Cited by 8 publications

References 63 publications

Modelling the neural code in large populations of correlated neurons

Modelling the neural code in large populations of correlated neurons

Modelling the neural code in large populations of correlated neurons

A neurally plausible model for online recognition and postdiction in a dynamical environment

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