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Related Experiment Videos

Computing and stability in cortical networks.

Peter E Latham1, Sheila Nirenberg

  • 1Department of Neurobiology, University of California at Los Angeles, Los Angeles, CA 90095-1763, USA. pel@gatsby.ucl.ac.uk

Neural Computation
|May 29, 2004
PubMed
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Cortical stability during computation is maintained by inhibition-dominated background states and sufficient neuronal recruitment for memory. This "dynamical stabilization" prevents runaway excitation in attractor networks.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Cortical neurons are highly interconnected and excitatory, posing a risk of runaway excitation.
  • Brain stability is maintained despite high neuronal firing rates during cognitive tasks.
  • Attractor networks, models of memory, exhibit multiple stable states.

Purpose of the Study:

  • To investigate how the cortex maintains stability during computations.
  • To understand the mechanisms stabilizing attractor networks at low firing rates.
  • To explore the role of background neuronal activity in cortical stability.

Main Methods:

  • Simulations of attractor networks.
  • Analysis of neuronal firing rates and network states.

Related Experiment Videos

  • Mathematical modeling of cortical dynamics.

    • Main Results:

    • Cortical stability is achieved when the background state is inhibition-dominated.
    • A sufficient fraction of neurons must be recruited for memory representation.
    • Dynamical stabilization by background neurons stabilizes otherwise unstable attractor states.

    Conclusions:

    • Dynamical stabilization is a key mechanism for maintaining cortical stability during computations.
    • This strategy allows stable network function at biologically observed low firing rates.
    • Dynamical stabilization may be broadly applicable to various cortical computations beyond attractors.