Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks

Author:

Nadafian Alireza1,Ganjtabesh Mohammad2

Affiliation:

1. School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran alirezanadafian@ut.ac.ir

2. School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran mgtabesh@ut.ac.ir

Abstract

Abstract The plasticity of the conduction delay between neurons plays a fundamental role in learning temporal features that are essential for processing videos, speech, and many high-level functions. However, the exact underlying mechanisms in the brain for this modulation are still under investigation. Devising a rule for precisely adjusting the synaptic delays could eventually help in developing more efficient and powerful brain-inspired computational models. In this article, we propose an unsupervised bioplausible learning rule for adjusting the synaptic delays in spiking neural networks. We also provide the mathematical proofs to show the convergence of our rule in learning spatiotemporal patterns. Furthermore, to show the effectiveness of our learning rule, we conducted several experiments on random dot kinematogram and a subset of DVS128 Gesture data sets. The experimental results indicate the efficiency of applying our proposed delay learning rule in extracting spatiotemporal features in an STDP-based spiking neural network.

Publisher

MIT Press

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Neuromorphic Temporal Pattern Detection with Spiking Neural Networks using Synaptic Delays;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

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