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Multistability in recurrent neural loops arising from delay.

J Foss1, J Milton

  • 1Committee on Neurobiology, The University of Chicago, Chicago, Illinois 60637, USA.

Journal of Neurophysiology
|August 12, 2000
PubMed
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Investigating neural loops reveals that time delays can cause multiple distinct firing patterns to coexist. This multistability in neural dynamics highlights the importance of considering loop propagation times.

Area of Science:

  • Computational Neuroscience
  • Neural Dynamics
  • Systems Neuroscience

Background:

  • Recurrent neural loops are fundamental to complex brain functions.
  • Understanding the impact of time delays on neural network dynamics is crucial.
  • Previous studies have not fully explored multistability in inhibitory neural loops with varying delays.

Purpose of the Study:

  • To investigate the dynamics of a recurrent inhibitory neural loop involving an Aplysia motoneuron and a computer.
  • To determine how the time delay (tau) affects the coexistence of different neural spike trains.
  • To develop and validate a mathematical model for this pulsed-coupled recurrent loop.

Main Methods:

  • Experimental investigation of an Aplysia motoneuron in a recurrent loop with a computer.

Related Experiment Videos

  • Systematic variation of the time delay (tau) for signal propagation.
  • Construction of a mathematical model based on experimentally measurable parameters like phase resetting curves.
  • Numerical simulations to explore model behavior and generalizability.

    • Main Results:

    • Multiple distinct neural spike trains were found to coexist for specific time delays (tau).
    • The mathematical model accurately reproduced the observed multistability.
    • Simulations suggest multistability becomes dominant as time delays increase.
    • Similar dynamics are expected with other neuron types.

    Conclusions:

    • Time delays in recurrent neural loops can lead to multistable dynamics, where multiple firing patterns coexist.
    • The developed mathematical model provides a framework for studying such systems.
    • Careful consideration of propagation times is essential when interpreting changes in neural spiking patterns, especially in polysynaptic loops.