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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

Brain-Inspired Architecture for Spiking Neural Networks.

Fengzhen Tang¹, Junhuai Zhang^1,2, Chi Zhang^1,2

¹State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, China.

Biomimetics (Basel, Switzerland)

|October 25, 2024

Summary

This study introduces a novel self-adaptive encoding spiking neural network (SNN) that integrates input processing, enhancing biological plausibility and performance in image classification tasks.

Keywords:

leaky integrate-and-fire neuron model self-adaptive coding spiking neural networks surrogate gradient backpropagation

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Area of Science:

Artificial Intelligence
Computational Neuroscience

Background:

Spiking neural networks (SNNs) mimic biological neurons but often require separate input preprocessing, risking information loss.
Biological neural systems process information without a distinct preprocessing step and utilize parallel pathways.

Purpose of the Study:

To develop a biologically plausible SNN that integrates input encoding within the network architecture.
To enhance SNN performance by incorporating parallel processing inspired by neural systems.

Main Methods:

Proposed a self-adaptive encoding SNN with a parallel architecture.
Integrated input-encoding via convolutional operations directly into the SNN.
Implemented two identical parallel branches for information processing.

Main Results:

The proposed SNN accepts real-valued input and automatically converts it to spikes.
Achieved competitive performance on multiple image classification tasks.
Demonstrated the effectiveness of the integrated encoding and parallel architecture.

Conclusions:

The novel SNN architecture effectively addresses information loss associated with separate preprocessing.
The integrated, parallel approach enhances SNN performance and biological plausibility.
This self-adaptive encoding SNN offers a promising direction for advanced neural network design.