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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Brain-inspired neural circuit evolution for spiking neural networks.

Guobin Shen1,2, Dongcheng Zhao1, Yiting Dong1,2

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This study introduces a brain-inspired method to evolve spiking neural networks with diverse neural circuits. This approach enhances performance in image classification and reinforcement learning tasks, advancing artificial intelligence capabilities.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current spiking neural networks (SNNs) often use deep learning structures, primarily feedforward, limiting their potential on complex tasks.
  • Biological neural systems exhibit self-organization and diverse neuron types, forming complex circuits for cognitive functions, a richness not fully captured in current SNN designs.
  • Integrating biological neural circuit dynamics into SNN structures remains a significant challenge.

Purpose of the Study:

  • To develop a more biologically plausible evolutionary framework for designing SNNs.
  • To enhance SNN capabilities by incorporating diverse neural circuit types and biologically inspired learning rules.
  • To improve performance in complex tasks like image classification and reinforcement learning.

Main Methods:

  • Proposed a novel evolutionary space combining feedforward and feedback connections with excitatory and inhibitory neurons.
  • Utilized local spike-timing-dependent plasticity (STDP) and global error signals to adaptively evolve neural circuits (e.g., forward/feedback inhibition, lateral inhibition).
  • Implemented the Neural circuit Evolution strategy (NeuEvo) to construct SNNs for image classification and reinforcement learning.

Main Results:

  • The evolved SNNs demonstrated significantly enhanced capabilities in perception and reinforcement learning tasks.
  • NeuEvo achieved state-of-the-art results on benchmark datasets including CIFAR10, DVS-CIFAR10, DVS-Gesture, and N-Caltech101, and advanced performance on ImageNet.
  • When combined with deep reinforcement learning algorithms, the evolved SNNs achieved performance comparable to traditional artificial neural networks.

Conclusions:

  • The brain-inspired NeuEvo strategy effectively evolves complex SNNs with rich neural circuit types, overcoming limitations of current SNN design paradigms.
  • This approach provides a foundation for creating more sophisticated artificial neural networks inspired by biological systems.
  • The evolved spiking neural circuits pave the way for future advancements in complex network evolution for diverse functional applications.