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Cited by 645Neural dynamics and brain functionMemory and Neural MechanismsVisual perception and processing mechanisms

Probabilistic population codes for Bayesian decision making

Open access
Beck, Jeffrey M., Ma, Wei Ji, Kiani, Roozbeh, Hanks, Tim, Churchland, Anne K., Roitman, Jamie, Shadlen, Michael N., Latham, Peter E. +1 more·2008·Neuron
DOI 10.1016/j.neuron.2008.09.021PMC PMC2742921
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Abstract

When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal's performance. We present experimental evidence consistent with this prediction and discuss other predictions applicable to more general settings.

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  • Bayesian inference can be performed in a purely feedforward manner.

    “While it may seem natural to implement priors with a feedback process, it should be noted that Bayesian inference can be performed in a purely feedforward manner [109,110].”

    From How Do Expectations Shape Perception? (2018) · cites this paper as [109]

    The Bayesian Formulation describes feedforward connections from MT to LIP for encoding a posterior, supporting the point that inference need not rely on feedback from later layers. But it also specifies lateral connections within LIP, and the Optimal Network section uses recurrent connectivity for response selection. The paper therefore does not establish a purely feedforward implementation of its full Bayesian decision-making model. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • These models are naturally viewed as an extension of signal detection theory and Bayesian inference to streams of data over time.

    “They are naturally viewed as an extension of signal detection theory and Bayesian inference to streams of data over time4,11.”

    From Changes of mind in decision-making (2009) · cites this paper as [11]

  • The agreement between model and neural responses is only approximate, presumably because neurons besides the ones recorded contribute to the estimation of certainty.

    “The agreement is only approximate, presumably because neurons besides the ones we recorded contribute to the estimation of certainty (1, 22).”

    From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [22]

  • It is probably also consistent with other models that exploit a broader population of LIP neurons to encode posterior probability.

    “It is probably also consistent with other models that exploit a broader population of LIP neurons to encode posterior probability (22, 41).”

    From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [22]

  • Recent models propose that uncertainty reduction depends on neural computations implementing weighted averaging of sensory estimates.

    “Recent models propose that uncertainty reduction depends on neural computations implementing weighted averaging of sensory estimates (24, 25).”

    From Fusion of visual cues is not mandatory in children (2010) · cites this paper as [25]

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Research works printed but not linked: 1

  1. [47]47. Wald, A. (1947). Sequential Analysis (New York: Wiley).

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Authors
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A.
Affiliations
  • University of Rochester (US)
  • Baylor College of Medicine (US)
  • Howard Hughes Medical Institute (US)
  • University of Illinois Chicago (US)
  • Gatsby Computational Neuroscience Unit (GB)
  • Collège de France (FR)
Corresponding author
Pouget, Alexandre
ORCID
Venue
Neuron
Publisher
Elsevier BV
Published
2008-12
Volume
60
Issue
6
Pages
1142-1152
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/j.neuron.2008.09.021
PMC
PMC2742921
OpenAlex
W2100259206
Cited by
645
References
54
Citations by year
2026: 14 · 2025: 15 · 2024: 29 · 2023: 31 · 2022: 37 · 2021: 33 · 2020: 37 · 2019: 38 · 2018: 41 · 2017: 39 · 2016: 34 · 2015: 44 · 2014: 43 · 2013: 38 · 2012: 52
FWCI
20.67
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • National Science Foundation (0446730, 0346785)
  • James S. McDonnell Foundation
  • Multidisciplinary University Research Initiative (N00014-07-1-0937)
  • National Institute of Mental Health (R01MH062447, R01 MH62447)
  • National Institute on Drug Abuse (R01DA022780)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
Bayesian decision making, evidence accumulation, action selection, attractor dynamics, linear integration, neural correlations, tuning curves, sensory reliability, population coding
MeSH terms
Action Potentials, Animals, Haplorhini, Bayes Theorem, Computer Simulation, Decision Making, Humans, Models, Neurological, Motion Perception, Neurons, Photic Stimulation, Reaction Time, Time Factors, Neural Networks, Computer, Nonlinear Dynamics

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