Inhibition SNN: unveiling the efficacy of various lateral inhibition learning in image pattern recognition

This study presents an enhanced spiking neural network (SNN) with inhibition, referred to as Inhibition SNN, which advances the field of pattern recognition through efficient event-driven computation. We incorporate a winner-take-all (WTA) mechanism into leaky integrate-and-fire neurons. This, combined with spike timing dependent plasticity, simulates the biological learning process. We propose two network prototypes, namely Inhibition V1 and Inhibition V2. Inhibition V1 differentiates between an excitatory layer and an inhibitory layer, while Inhibition V2 uses self-connections in the excitatory layer to implement WTA dynamics, enhancing signal contrast without a separate inhibitory layer. Our experiments on the MNIST dataset show that both Inhibition V1 and Inhibition V2 can match the accuracy of current advanced networks, with Inhibition V2 performing better. An unsupervised learning network of two layers (784–100) achieves 86% accuracy on MNIST and 61% on Fashion-MNIST. The scalability and robustness of these networks in neural computation underscore their potential for practical applications. This research offers a fresh perspective on SNN design, highlighting the impact of inhibition on learning efficiency. With plans to adapt these networks to neuromorphic hardware, Inhibition SNN could pave the way for energy-efficient intelligent systems.