Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making

A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a...

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Vydáno v:Frontiers in neuroscience Ročník 11; s. 313
Hlavní autoři: Daniels, Bryan C., Flack, Jessica C., Krakauer, David C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Research Foundation 06.06.2017
Frontiers Media S.A
Témata:
Coding theory
Cognitive ability
collective computation
critical slowing down
Decision making
decision tasks
Eye movements
Firing pattern
Information processing
Linear algebra
Motion detection
Nervous system
Neural coding
Neurons
Neuroscience
Visual discrimination
United States > US
collective computation
decision tasks
critical slowing down
ISSN:1662-453X, 1662-4548, 1662-453X
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Shrnutí:A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a "coding duality" in which there are accumulation and consensus formation processes distinguished by different timescales.
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Reviewed by: Markus Siegel, University of Tübingen, Germany; Asif A. Ghazanfar, Princeton University, United States
This article was submitted to Decision Neuroscience, a section of the journal Frontiers in Neuroscience
Edited by: Tobias H. Donner, University Medical Center Hamburg-Eppendorf, Germany
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2017.00313