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Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex.

John D Murray1, Alberto Bernacchia2, Nicholas A Roy3

  • 1Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510.

Proceedings of the National Academy of Sciences of the United States of America
|December 29, 2016
PubMed
Summary
This summary is machine-generated.

Despite complex single-neuron dynamics, prefrontal cortex population activity stably encodes working memory (WM) information. This stability enables robust stimulus representation crucial for cognitive functions.

Keywords:
population codingprefrontal cortexworking memory

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Working memory (WM) involves maintaining and manipulating information, requiring internal representations during delays.
  • Neurons in the primate prefrontal cortex (PFC) show stimulus-selective activity during WM, but exhibit temporal dynamics and heterogeneity.
  • The stability of mnemonic representations in PFC despite neural heterogeneity remains an open question.

Purpose of the Study:

  • To investigate whether and how neuronal populations in the PFC maintain stable mnemonic representations during WM.
  • To analyze population-level coding in PFC during tasks requiring parametric WM.
  • To explore potential neural circuit mechanisms underlying stable WM representations.

Main Methods:

  • Population-level analyses of hundreds of single neurons recorded from monkey lateral PFC.
  • Application of analyses to oculomotor delayed response and vibrotactile delayed discrimination tasks.
  • Analysis of theoretical neural circuit models of WM activity.

Main Results:

  • PFC population activity exhibits a stable, low-dimensional subspace for mnemonic stimulus representation across time.
  • This stable subspace enables robust and generalizable decoding of stimuli during WM maintenance.
  • Previously proposed WM models failed to replicate observed population-level features.

Conclusions:

  • PFC population activity provides a stable and robust code for mnemonic stimuli, despite complex single-neuron dynamics.
  • A linear network model with specific connectivity properties can explain these population-level features.
  • This study offers insights into the neural circuit mechanisms supporting working memory in the PFC.