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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Vydáno v:	PLoS computational biology Ročník 14; číslo 4; s. e1006033
Hlavní autoři:	Caballero, Javier A., Humphries, Mark D., Gurney, Kevin N.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 01.04.2018 Public Library of Science (PLoS)
Témata:	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
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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