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Damage explains function in spiking neural networks representing central pattern generator.

Yuriy Pryyma1, Sergiy Yakovenko2,3,4,5,6

  • 1Faculty of Applied Science, Ukrainian Catholic University, Lviv, Ukraine.

Journal of Neural Engineering
|December 3, 2024
PubMed
Summary
This summary is machine-generated.

This study models locomotor control using a spiking neural network (SNN) central pattern generator (CPG). It reveals how simulated damage affects neural computations and how external drive can restore function.

Keywords:
CPGcentral pattern generatordamagelocomotionmodelspiking neural network

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Robotics

Background:

  • Locomotion control in complex biological systems relies on predictive mechanisms to overcome noisy inputs and delays.
  • Vertebrate locomotion involves interactions between passive dynamics and neural oscillators, notably the central pattern generator (CPG).
  • Existing rate models of CPGs accurately describe gait patterns, but the neural computations in spiking neural networks (SNNs) remain less understood.

Purpose of the Study:

  • To develop a spiking neural network (SNN) model of a locomotor central pattern generator (CPG).
  • To investigate the impact of simulated neural circuit damage on distributed computations.
  • To explore the potential of external drive to restore function after damage.

Main Methods:

  • Developed a locomotor CPG model using a spiking neural network (SNN) architecture.
  • Validated the SNN-CPG model against established rate models for gait phase modulation.
  • Simulated progressive lesions within the SNN-CPG to analyze computational degradation.

Main Results:

  • The SNN-CPG model accurately replicated the input-output dynamics of rate models.
  • Simulated lesions demonstrated differential effects on flexor and extensor functions, consistent with experimental observations.
  • Increased external drive effectively compensated for structural damage and restored locomotor function.

Conclusions:

  • The study provides theoretical insights into the network dynamics of locomotor control.
  • Introduces the concept of 'degraded computations' in neural circuits.
  • Highlights the potential of external drive for restorative technologies in locomotion.