Probabilistic decision making by slow reverberation in cortical circuits

Wang, Xiao-Jing

doi:10.1016/s0896-6273(02)01092-9

Cited by 1,242Neural dynamics and brain functionVisual perception and processing mechanismsNeuroscience and Neuropharmacology Research

Probabilistic decision making by slow reverberation in cortical circuits

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Wang, Xiao-Jing·2002·Neuron

DOI 10.1016/s0896-6273(02)01092-9

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Abstract

Recent physiological studies of alert primates have revealed cortical neural correlates of key steps in a perceptual decision-making process. To elucidate synaptic mechanisms of decision making, I investigated a biophysically realistic cortical network model for a visual discrimination experiment. In the model, slow recurrent excitation and feedback inhibition produce attractor dynamics that amplify the difference between conflicting inputs and generates a binary choice. The model is shown to account for salient characteristics of the observed decision-correlated neural activity, as well as the animal's psychometric function and reaction times. These results suggest that recurrent excitation mediated by NMDA receptors provides a candidate cellular mechanism for the slow time integration of sensory stimuli and the formation of categorical choices in a decision-making neocortical network.

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The single latent variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound.
“We show that this variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [26]
The attractor model of decision-making is a neuronal network with associatively modifiable recurrent collateral synapses between neurons of the type prototypical of the cerebral cortex.
“The attractor model of decision-making is a neuronal network with associatively modifiable recurrent collateral synapses between the neurons of the type prototypical of the cerebral cortex (Wang, 2002; Rolls and Deco, 2010; Rolls, 2021a).”
From The orbitofrontal cortex: reward, emotion and depression (2020) · cites this paper as Wang, 2002
In a two-pool version of the recurrent neural circuit model, subpopulations of spiking neurons are selective for two-choice alternatives.
“In a two-pool version of the model, subpopulations of spiking neurons are selective for two-choice alternatives (e.g., A = left motion, B = right motion).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002
Within each pyramidal neural group, strong recurrent excitatory connections can sustain persistent activity triggered by a transient preferred stimulus.
“Within each pyramidal neural group, strong recurrent excitatory connections can sustain persistent activity triggered by a transient preferred stimulus.”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002
The two neural groups compete through feedback inhibition from interneurons.
“The two neural groups compete through feedback inhibition from interneurons.”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002

The common occurrence of decision-related activity and mnemonic persistent activity suggests a common circuit mechanism underlying decision making and working memory.
“Motivated by this observation, it has been proposed that this is not a mere coincidence, but suggests a common circuit mechanism underlying decision making and working memory (Wang, 2002).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002
In the recurrent circuit model, competition between neural pools selective for choice alternatives is instantiated by lateral synaptic inhibition.
“In the recurrent circuit model, competition between neural pools selective for choice alternatives is instantiated by lateral synaptic inhibition (Wang, 2002; Wong and Wang, 2006).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002
In the attractor network model, the influence of newly arriving inputs diminishes over time as the network converges toward one of the attractor states representing the alternative choices.
“Third, the attractor network model has been shown to be able to subtract negative signals as well as add positive evidence about choice alternatives, but the influence of newly arriving inputs diminishes over time, as the network converges toward one of the attractor states representing the alternative choices (Wang, 2002).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002
In the recurrent circuit model, performance plateaus at sufficiently long stimulus-viewing times.
“Consequently performance plateaus at sufficiently long stimulus-viewing times (Wang, 2002).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002
The network displays transient dynamics of ramping activity on a timescale of up to 1 s, and this ability critically depends on NMDA receptors.
“Thus, the network displays transient dynamics of ramping activity on a timescale of up to 1 s, and this ability critically depends on the NMDA receptors (Wang, 2002; Wong and Wang, 2006).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002
The attractor network model can subtract negative signals as well as add positive evidence about choice alternatives.
“Third, the attractor network model has been shown to be able to subtract negative signals as well as add positive evidence about choice alternatives, but the influence of newly arriving inputs diminishes over time, as the network converges toward one of the attractor states representing the alternative choices (Wang, 2002).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Wang, 2002

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“Hence, these new findings, although seemingly contradicting earlier views, emphasize the role of feedback loops through which sensory information reverberates (26) before transforming into perception and action.”

Authors: Wang, X.-J.
Affiliations: Brandeis University (US)
Corresponding author: Wang, Xiao-Jing
ORCID: Wang, Xiao-Jing
Venue: Neuron
Publisher: Elsevier BV
Published: 2002-12
Volume: 36
Issue: 5
Pages: 955-968
ISSN: 0896-6273 (ISSN-L)
DOI: 10.1016/s0896-6273(02)01092-9
OpenAlex: W2168815228
Semantic Scholar: 6e88e73f1e6a8328105fe80d547e6a3e4c2e460d
Cited by: 1,242
References: 66
Citations by year: 2026: 68 · 2025: 78 · 2024: 53 · 2023: 63 · 2022: 76 · 2021: 79 · 2020: 63 · 2019: 65 · 2018: 55 · 2017: 62 · 2016: 73 · 2015: 59 · 2014: 60 · 2013: 59 · 2012: 52
FWCI: 4.17
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 3881 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Neuron (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Keywords: perceptual decision making, cortical circuits, recurrent excitation, product inhibition, attractor dynamics, NMDA receptor, time integration, visual discrimination, reaction time, psychometric functions
MeSH terms: Animals, Cerebral Cortex, Decision Making, Mathematics, Models, Neurological, Neurons, Reaction Time, Synapses, Receptors, N-Methyl-D-Aspartate, Neural Networks, Computer

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Probabilistic decision making by slow reverberation in cortical circuits

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