# Locally Connected Spiking Neural Networks for Unsupervised Feature Learning

## TL;DR

This paper introduces Locally-Connected Spiking Neural Networks (LC-SNNs) that learn image features using STDP, achieving state-of-the-art accuracy with fast convergence, fewer parameters, and robustness to synapse and neuron deletion.

## Contribution

The paper presents a novel LC-SNN architecture with biologically inspired local connectivity and competitive learning, outperforming previous SNN models in unsupervised image classification.

## Key findings

- Achieved state-of-the-art accuracy on two image datasets.
- Fast convergence with fewer learnable parameters.
- Robust performance despite synapse and neuron removal.

## Abstract

In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by \textit{locally connected layers} in SNNs using spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks compete via competitive inhibitory interactions to learn features from different locations of the input space. These \textit{Locally-Connected SNNs} (LC-SNNs) manifest key topological features of the spatial interaction of biological neurons. We explore biologically inspired n-gram classification approach allowing parallel processing over various patches of the the image space. We report the classification accuracy of simple two-layer LC-SNNs on two image datasets, which match the state-of-art performance and are the first results to date. LC-SNNs have the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Robustness tests demonstrate that LC-SNNs exhibit graceful degradation of performance despite the random deletion of large amounts of synapses and neurons.