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A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks.

Xinyi Chen, Qu Yang, Jibin Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 6, 2023

Summary

This study introduces hybrid neural coding for spiking neural networks (SNNs), enhancing performance. These brain-inspired models offer improved accuracy, speed, and efficiency for complex tasks.

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Neuromorphic Engineering

Background:

Spiking neural networks (SNNs) show promise in pattern recognition.
Current SNNs use uniform neural coding, limiting performance (accuracy, speed, efficiency, robustness).

Purpose of the Study:

To propose a hybrid neural coding framework for SNNs.
To explore heterogeneous coding schemes for improved SNN performance.

Main Methods:

Developed a framework with a 'neural coding zoo' of diverse schemes.
Implemented flexible coding assignment and novel layer-wise learning methods.
Tested on image classification and sound localization.

Main Results:

Achieved comparable accuracy to state-of-the-art SNNs.
Significantly reduced inference latency and energy consumption.
Demonstrated high noise robustness.

Conclusions:

Hybrid neural coding significantly enhances SNN performance.
This approach is crucial for developing high-performance neuromorphic systems.
Provides insights for future hybrid coding designs.