Modeling of Spiking Neural Network With Optimal Hidden Layer via Spatiotemporal Orthogonal Encoding for Patterns Recognition

Abstract

The Spiking Neural Network (SNN) diverges from conventional rate-based network models by showcasing remarkable biological fidelity and advanced spatiotemporal computation capabilities, precisely converting input spike sequences into firing activities. This paper introduces the Spiking Optimal Neural Network (SONN), a model that integrates spiking neurons with spatiotemporal orthogonal polynomials to enhance pattern recognition capabilities. SONN innovatively integrates orthogonal polynomials and complex domain transformations seamlessly into neural dynamics, aiming to elucidate neural encoding and enhance cognitive computing capabilities. The dynamic integration of SONN enables continuous optimization of encoding methodologies and layer structures, showcasing its adaptability and refinement over time. Fundamentally, the model provides an adjustable method based on orthogonal polynomials and the corresponding complex-valued neuron model, striking a balance between network scalability and output accuracy. To evaluate its performance, SONN underwent experiments using datasets from the UCI Machine Learning Repository, the Fashion-MNIST dataset, the CIFAR-10 dataset and neuromorphic DVS128 Gesture dataset. The results show that smaller-sized SONN architectures achieve comparable accuracy in benchmark datasets compared to other SNNs.