Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks
This work addresses the problem of understanding and implementing synaptic delay modulation for developing more effective brain-inspired computational models, which is an incremental step in SNN research.
This paper proposes an unsupervised, biologically plausible learning rule for adjusting synaptic delays in spiking neural networks. The rule enables neurons to learn repeating spatio-temporal patterns, and experiments on Random Dot Kinematogram show its efficacy in extracting temporal features.
The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an open problem. Understanding the precise adjustment of synaptic delays could help us in developing effective brain-inspired computational models in providing aligned insights with the experimental evidence. In this paper, we propose an unsupervised biologically plausible learning rule for adjusting the synaptic delays in spiking neural networks. Then, we provided some mathematical proofs to show that our learning rule gives a neuron the ability to learn repeating spatio-temporal patterns. Furthermore, the experimental results of applying an STDP-based spiking neural network equipped with our proposed delay learning rule on Random Dot Kinematogram indicate the efficacy of the proposed delay learning rule in extracting temporal features.