# Efficient single input-output layer spiking neural classifier with time-varying weight model

## TL;DR

This paper introduces SEF-M, a supervised learning algorithm for a single-layer spiking neural network with a novel time-varying weight model, demonstrating improved accuracy and efficiency over existing methods on benchmark datasets.

## Contribution

The paper proposes a new learning algorithm, SEF-M, that employs a time-varying weight model in spiking neural networks, enhancing classification accuracy and computational efficiency.

## Key findings

- SEF-M outperforms state-of-the-art algorithms on benchmark datasets.
- Using a time-varying weight model improves accuracy by 14%.
- Single-layer SNN with time-varying weights is more efficient than multi-layer models.

## Abstract

This paper presents a supervised learning algorithm, namely, the Synaptic Efficacy Function with Meta-neuron based learning algorithm (SEF-M) for a spiking neural network with a time-varying weight model. For a given pattern, SEF-M uses the learning algorithm derived from meta-neuron based learning algorithm to determine the change in weights corresponding to each presynaptic spike times. The changes in weights modulate the amplitude of a Gaussian function centred at the same presynaptic spike times. The sum of amplitude modulated Gaussian functions represents the synaptic efficacy functions (or time-varying weight models). The performance of SEF-M is evaluated against state-of-the-art spiking neural network learning algorithms on 10 benchmark datasets from UCI machine learning repository. Performance studies show superior generalization ability of SEF-M. An ablation study on time-varying weight model is conducted using JAFFE dataset. The results of the ablation study indicate that using a time-varying weight model instead of single weight model improves the classification accuracy by 14%. Thus, it can be inferred that a single input-output layer spiking neural network with time-varying weight model is computationally more efficient than a multi-layer spiking neural network with long-term or short-term weight model.