Using a simple neural network to delineate some principles of distributed economic choice

Pragathi P. Balasubramani, Rubén Moreno-Bote, Benjamin Y. Hayden

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to performbinary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation frominput to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Original languageEnglish (US)
Article number22
JournalFrontiers in Computational Neuroscience
Volume12
DOIs
StatePublished - Mar 28 2018

Bibliographical note

Funding Information:
This work was supported by an R01 from NIH to BH(DA037229) and grants PSI2013-44811-P and FLAGERA-PCIN-2015-162-C02-02 from MINECO(Spain) to RM-B.

Keywords

  • Distributed network
  • Modular network
  • Neural network
  • Neuroeconomics
  • Parallel distributed system

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