Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network

• URL: http://arxiv.org/abs/2003.02944v2
• Date: Sun, 26 Jul 2020 03:39:24 GMT
• Title: Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network
• Authors: Haowen Fang, Amar Shrestha, Ziyi Zhao, Qinru Qiu
• Abstract summary: A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. We formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We propose a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights.
• Score: 7.503685643036081
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. Each synapse and neuron behave as filters capable of preserving temporal information. As such neuron dynamics and filter effects are ignored in existing training algorithms, the SNN downgrades into a memoryless system and loses the ability of temporal signal processing. Furthermore, spike timing plays an important role in information representation, but conventional rate-based spike coding models only consider spike trains statistically, and discard information carried by its temporal structures. To address the above issues, and exploit the temporal dynamics of SNNs, we formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We proposed a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights. The proposed model and training algorithm are applied to construct associative memories and classifiers for synthetic and public datasets including MNIST, NMNIST, DVS 128 etc.; and their accuracy outperforms state-of-art approaches.

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