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Model-agnostic linear-memory online learning in spiking neural networks.

Chaoming Wang1, Xingsi Dong2,3, Zilong Ji4

  • 1Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong, China. wangchaoming@gdiist.cn.

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BrainTrace is a new online learning system for spiking neural networks (SNNs). It enables efficient training of complex brain dynamics with low memory use, advancing neuromorphic intelligence.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • Spiking neural networks (SNNs) show promise for brain dynamics and neuromorphic intelligence.
  • Existing online learning systems for SNNs face challenges with memory, biological fidelity, and automation.
  • A need exists for efficient, scalable, and automated online learning for SNNs.

Purpose of the Study:

  • To introduce BrainTrace, a novel model-agnostic, linear-memory, and automated online learning system for SNNs.
  • To address limitations of current SNN online learning methods.
  • To enable large-scale SNN modeling and analysis.

Main Methods:

  • BrainTrace standardizes SNN model specification for diverse neuronal and synaptic dynamics.
  • A linear-memory online learning rule is implemented by leveraging intrinsic spiking dynamics properties.
  • An automated compiler generates optimized online-learning code for user-defined SNN models.

Main Results:

  • BrainTrace demonstrates strong learning performance across various dynamics and tasks with low memory footprint and high computational throughput.
  • The system enables online fitting of a whole-brain-scale Drosophila SNN.
  • The fitted Drosophila SNN successfully recapitulates region-level functional activity.

Conclusions:

  • BrainTrace reconciles generality, computational efficiency, and usability in SNN online learning.
  • It provides a foundational tool for large-scale spiking network modeling.
  • BrainTrace advances the development of neuromorphic intelligence and brain dynamics research.