A probabilistic, distributed, recursive mechanism for decision-making in the brain

Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT)....

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Bibliographic Details
Published in:PLoS computational biology Vol. 14; no. 4; p. e1006033
Main Authors: Caballero, Javier A., Humphries, Mark D., Gurney, Kevin N.
Format: Journal Article
Language:English
Published: United States Public Library of Science 01.04.2018
Public Library of Science (PLoS)
Subjects:
Algorithms
Animals
Basal ganglia
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Brain
Brain - anatomy & histology
Brain - physiology
Brain Mapping
Brain research
Computational Biology
Computer Simulation
Corpus Striatum - physiology
Cortex (somatosensory)
Decision making
Decision Making - physiology
Decisions
Economic forecasting
Electrophysiological Phenomena
Electrophysiology
Funding
Ganglia
Haplorhini - anatomy & histology
Haplorhini - physiology
Haplorhini - psychology
Mathematical models
Medicine and Health Sciences
Models, Neurological
Models, Psychological
Models, Statistical
Monkeys
Monkeys & apes
Neostriatum
Neural circuitry
Physiological aspects
Probabilistic inference
Reaction Time - physiology
Sensorimotor Cortex - physiology
Social Sciences
Somatosensory cortex
Statistical analysis
Thalamus
United Kingdom > UK
ISSN:1553-7358, 1553-734X, 1553-7358
Online Access:Get full text
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Description
Summary:Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.
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These authors are joint senior authors on this work.
The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1006033