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Updated: Jul 5, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Sampling complex topology structures for spiking neural networks.

Shen Yan¹, Qingyan Meng², Mingqing Xiao³

¹Center for Data Science, Peking University, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 20, 2024

Summary

This summary is machine-generated.

This study introduces a novel topology-aware search space and spatio-temporal topology sampling (STTS) algorithm for Spiking Neural Networks (SNNs). The method enhances SNN architecture design, achieving superior ImageNet accuracy with fewer time steps.

Keywords:

Neural architecture search Spiking neural networks

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Area of Science:

Artificial Intelligence
Computational Neuroscience

Background:

Spiking Neural Networks (SNNs) offer biological plausibility and energy efficiency but lack well-studied architecture design.
Existing SNN architectures often adapt Artificial Neural Network (ANN) designs or use constrained search spaces.

Purpose of the Study:

To explore more complex connection topologies in SNNs for improved performance.
To introduce a flexible and diverse design space for SNN spatial and temporal architectures.

Main Methods:

Proposing a novel topology-aware search space for SNN architecture design.
Developing the Spatio-Temporal Topology Sampling (STTS) algorithm for efficient architecture discovery via random sampling.

Main Results:

Demonstrated effectiveness on CIFAR-10, CIFAR-100, and ImageNet datasets.
Achieved 70.79% top-1 accuracy on ImageNet with only 4 time steps, surpassing prior methods by 1.79%.

Conclusions:

The proposed topology-aware search space and STTS algorithm significantly advance SNN architecture design.
STTS offers an efficient alternative to exhaustive search, yielding high-performing SNN architectures.