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Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks
Yana Garipova1, Shogo Yonekura1, Yasuo Kuniyoshi1
1Laboratory for Intelligent Systems and Informatics, University of Tokyo, Tokyo 113-0033, Japan.
Abstract:
Standard ANNs lack flexibility when handling corrupted input due to their fixed structure. In this paper, a spiking neural network utilizes biological temporal coding features in the form of noise-induced stochastic resonance and dynamical synapses to increase the model's performance when its parameters are not optimized for a given input. Using the analog XOR task as a simplified convolutional neural network model, this paper demonstrates two key results: (1) SNNs solve the problem that is linearly inseparable in ANN with fewer neurons, and (2) in leaky SNNs, the addition of noise and dynamical synapses compensate for non-optimal parameters, achieving near-optimal results for weaker inputs.
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