Mapping low-dimensional dynamics to high-dimensional neural activity: A derivation of the ring model from the neural engineering framework

Omri Barak, Sandro Romani

Research output: Contribution to journalLetterpeer-review

3 Scopus citations

Abstract

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity—the neural engineering framework. We analytically solve the framework for the classic ring model—a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

Original languageEnglish
Article number3
Pages (from-to)827-852
Number of pages26
JournalNeural Computation
Volume33
Issue number3
DOIs
StatePublished - Mar 2021

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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