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

Bryan C Daniels,Jessica C Flack,David C Krakauer

doi:10.3389/fnins.2017.00313

Bryan C Daniels, Jessica C Flack + Show 1 more

Open Access

https://doi.org/10.3389/fnins.2017.00313

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Abstract

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.

Highlights

IntroductionFunctional encodings have been identified at the level of single cells (e.g., Shadlen and Newsome, 2001), correlated modules (e.g., Power et al, 2013; Gu et al, 2015), and hemispheres (e.g., Doron et al, 2012)
The nervous system is a distributed information processing system
Our findings lead us to propose a coding-duality framework, applicable to collective computation in adaptive systems more generally, that includes a slow accumulation process in which information is encoded in populations and a fast consensus formation process in which information is encoded redundantly in multiple individual neurons

Summary

Introduction

Functional encodings have been identified at the level of single cells (e.g., Shadlen and Newsome, 2001), correlated modules (e.g., Power et al, 2013; Gu et al, 2015), and hemispheres (e.g., Doron et al, 2012). How activity within a scale produces new functional encodings one level up and how the consolidating modules interact to produce coherent, functional behavioral output at the whole brain level are among the primary concerns of cognitive neuroscience (e.g., Gu et al, 2015). One is a “distributed perspective”—coherent output requires encoding the output over many cells (“population-level coding”). The second favors localization—coherent output can be generated by encoding the output by strong activity in one or a few neurons (“grandmother neurons,” reviewed in Gross, 2002, or “sparse coding” Quian Quiroga and Kreiman, 2010)

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