Spatiotemporal contextual learning network with excitatory and inhibitory synapses for spiking neural network hardware
Takemori Orima, Yoshihiko Horio, Takeru Tsuji · Nonlinear Theory and Its Applications IEICE · 2024
A spatiotemporal contextual learning network (STCLN) model is suitable for edge applications. To implement the STCLN model as a small analog asynchronous integrated circuit with low power consumption, this study proposes to introduce the excitatory and inhibitory synapses into the STCLN model which can be implemented by an event-driven spiking neural network (SNN) circuit. Through the performance evaluation of the proposed STCLN model for SNN, the optimal ratio of the excitatory and inhibitory synapse is derived.