# A Spiking Neural Network with Local Learning Rules Derived From Nonnegative Similarity Matching

## TL;DR

This paper introduces a biologically plausible spiking neural network algorithm derived from nonnegative similarity matching, capable of unsupervised sparse feature extraction and manifold learning, suitable for neuromorphic hardware.

## Contribution

It provides a principled derivation of a local learning rule-based spiking neural network from a nonnegative similarity matching framework.

## Key findings

- Performs sparse feature extraction effectively
- Capable of manifold learning tasks
- Suitable for implementation on neuromorphic hardware

## Abstract

The design and analysis of spiking neural network algorithms will be accelerated by the advent of new theoretical approaches. In an attempt at such approach, we provide a principled derivation of a spiking algorithm for unsupervised learning, starting from the nonnegative similarity matching cost function. The resulting network consists of integrate-and-fire units and exhibits local learning rules, making it biologically plausible and also suitable for neuromorphic hardware. We show in simulations that the algorithm can perform sparse feature extraction and manifold learning, two tasks which can be formulated as nonnegative similarity matching problems.