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Related Experiment Videos

Neural integrator: a sandpile model.

Maxim Nikitchenko1, Alexei Koulakov

  • 1Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.

Neural Computation
|June 7, 2008
PubMed
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This study models neural integrators using hysteretic neurons. Recurrent connections can reverse hysteresis direction, impacting eye movement control and demonstrating novel integration properties.

Area of Science:

  • Computational neuroscience
  • Neural modeling
  • Systems neuroscience

Background:

  • Neural integrators are crucial for maintaining neural activity, particularly in oculomotor control.
  • Hysteresis, a memory-like property, is proposed to arise from intrinsic cellular properties in neural integrator models.
  • Recurrent neural networks with bistable or multistable neurons offer a framework for exploring complex neural dynamics.

Purpose of the Study:

  • To investigate a neural integrator model based on hysteretic units with positive feedback.
  • To analyze how recurrent connections influence hysteresis direction in neural networks.
  • To explore the relationship between macroscopic integrator properties and microscopic neuronal dynamics.

Main Methods:

  • Development of a computational model for hysteretic neural integrators.

Related Experiment Videos

  • Analysis of recurrent networks comprising bistable or multistable neurons.
  • Examination of the system's behavior in parameter space, focusing on the oculomotor velocity-to-position integrator.

    • Main Results:

    • Demonstrated that recurrent connections can reverse the direction of neuronal response hysteresis compared to unconnected neurons.
    • Showed that for NMDA receptor-based bistability, firing rates after ON saccades can exceed those after OFF saccades at identical eye positions.
    • Identified that hysteresis reversal occurs when individual neuron hysteresis magnitudes vary within the network.
    • Related the integrator's macroscopic leak time constant to spontaneous noise-driven transitions in hysteretic units.
    • Investigated conditions for a hysteretic integrator operating without an integration threshold.

    Conclusions:

    • Recurrent connectivity in hysteretic neural networks can fundamentally alter response dynamics, including hysteresis direction.
    • Variability in neuronal hysteresis is a key factor for hysteresis reversal, with implications for neural computation.
    • The model links macroscopic integrator function to microscopic noise dynamics, offering insights into neural control mechanisms.