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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Vydané v:PLoS computational biology Ročník 14; číslo 4; s. e1006033
Hlavní autori: Caballero, Javier A., Humphries, Mark D., Gurney, Kevin N.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 01.04.2018
Public Library of Science (PLoS)
Predmet:
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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Abstract 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.
AbstractList 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. Decision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decision-making has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decision-making in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.
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.
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.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.
Audience Academic
Author Caballero, Javier A.
Gurney, Kevin N.
Humphries, Mark D.
AuthorAffiliation 1 Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
2 Deptartment of Psychology, The University of Sheffield, Sheffield, United Kingdom
Brain and Spine Institute (ICM), FRANCE
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CitedBy_id crossref_primary_10_7554_eLife_60628
crossref_primary_10_7554_eLife_65540
crossref_primary_10_1007_s00422_021_00887_5
crossref_primary_10_1038_s41598_021_87191_1
crossref_primary_10_1371_journal_pcbi_1006998
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ContentType Journal Article
Copyright COPYRIGHT 2018 Public Library of Science
2018 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Caballero JA, Humphries MD, Gurney KN (2018) A probabilistic, distributed, recursive mechanism for decision-making in the brain. PLoS Comput Biol 14(4): e1006033. https://doi.org/10.1371/journal.pcbi.1006033
2018 Caballero et al 2018 Caballero et al
2018 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Caballero JA, Humphries MD, Gurney KN (2018) A probabilistic, distributed, recursive mechanism for decision-making in the brain. PLoS Comput Biol 14(4): e1006033. https://doi.org/10.1371/journal.pcbi.1006033
Copyright_xml – notice: COPYRIGHT 2018 Public Library of Science
– notice: 2018 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Caballero JA, Humphries MD, Gurney KN (2018) A probabilistic, distributed, recursive mechanism for decision-making in the brain. PLoS Comput Biol 14(4): e1006033. https://doi.org/10.1371/journal.pcbi.1006033
– notice: 2018 Caballero et al 2018 Caballero et al
– notice: 2018 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Caballero JA, Humphries MD, Gurney KN (2018) A probabilistic, distributed, recursive mechanism for decision-making in the brain. PLoS Comput Biol 14(4): e1006033. https://doi.org/10.1371/journal.pcbi.1006033
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Snippet Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a...
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StartPage e1006033
SubjectTerms 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
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Title A probabilistic, distributed, recursive mechanism for decision-making in the brain
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Volume 14
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