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Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems.

Wenzhe Guo1,2, Mohammed E Fouda2,3, Ahmed M Eltawil2,3

  • 1Sensors Laboratory, Advanced Membranes and Porous Materials Center (AMPMC), Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Frontiers in Neuroscience
|March 22, 2021
PubMed
Summary

This study compares four neural coding schemes for brain-inspired spiking neural networks (SNNs). Time-to-first spike (TTFS) coding offers superior computational performance and low hardware overhead, making it ideal for neuromorphic systems.

Keywords:
burst codingneural codesneuromorphic computingphase codingrate codingspiking neural networkstime to first spike codingunsupervised learning

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Area of Science:

  • Computational Neuroscience
  • Neuromorphic Engineering
  • Artificial Intelligence

Background:

  • Neural coding is crucial for information transmission in the brain and for the functionality of brain-inspired Spiking Neural Networks (SNNs).
  • Understanding the performance trade-offs of different neural coding schemes is essential for designing efficient neuromorphic systems.

Purpose of the Study:

  • To conduct an extensive comparative study of four prominent neural coding schemes: rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding.
  • To evaluate these schemes based on classification accuracy, processing latency, synaptic operations, hardware implementation, compression efficacy, and resilience to noise and faults.

Main Methods:

  • A biological 2-layer SNN was trained using an unsupervised spike-timing-dependent plasticity (STDP) algorithm.
  • Classification tasks were performed on MNIST and Fashion-MNIST datasets.
  • Hardware implementation (area, power), network compression (pruning, quantization), and noise/fault tolerance were analyzed for each coding scheme.

Main Results:

  • Time-to-first spike (TTFS) coding demonstrated the highest computational performance with minimal hardware overhead, showing significantly lower processing latency and synaptic operations compared to rate coding.
  • Phase coding exhibited the greatest resilience to input noise.
  • Burst coding provided the highest network compression efficacy and superior robustness against hardware non-idealities.

Conclusions:

  • The choice of neural coding scheme significantly impacts SNN performance, hardware requirements, and robustness.
  • TTFS coding is recommended for high computational performance and efficiency.
  • Phase and burst coding offer specific advantages in noise resilience and hardware robustness, respectively, providing a nuanced design space for neuromorphic systems.