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Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network.

Caelen J Hilty1,2,3, Paul Miller3,4

  • 1Neuroscience Program, Brandeis University, Waltham, Massachusetts, United States of America.

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Summary
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Neural circuits require history-dependent responses for effective information processing. This study shows inhibition-stabilized networks maintain computational abilities at low firing rates, crucial for cognitive tasks.

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Area of Science:

  • Computational neuroscience
  • Neural circuit dynamics
  • Cognitive function

Background:

  • Effective information processing relies on stimulus and prior history.
  • Existing attractor network models lack biologically realistic firing rates.
  • Recurrent excitatory networks struggle with stable low firing rates.

Purpose of the Study:

  • To demonstrate how inhibition-stabilized networks can achieve computational abilities of recurrent networks.
  • To show stabilization at biologically realistic low firing rates.
  • To explore the functional role of inhibitory stabilization in history-dependent computation.

Main Methods:

  • Modeling randomly connected inhibition-stabilized attractor networks.
  • Analyzing network activity and firing rates.
  • Investigating the impact of inhibitory feedback on computation.

Main Results:

  • Inhibition-stabilized networks preserve computational abilities of recurrent excitatory networks.
  • Network activity stabilizes at arbitrarily low firing rates.
  • Transient oscillations due to inhibitory feedback enable state-dependent responses.

Conclusions:

  • Excitatory-inhibitory balance stabilizes neural network activity.
  • Inhibitory stabilization is functionally important for history-dependent computation.
  • Inhibition-stabilized dynamics may underlie cognitive tasks and cortical computation.