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

arXiv: https://arxiv.org/abs/1902.01429 · HTML: https://mlsanity.com/paper/1902.01429/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [A Normative and Biologically Plausible Algorithm for Independent Component Analysis](https://mlsanity.com/paper/2111.08858/) (2021)
- [Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources](https://mlsanity.com/paper/2209.12894/) (2022)
- [A Neural Network with Local Learning Rules for Minor Subspace Analysis](https://mlsanity.com/paper/2102.05501/) (2021)
- [Learning Using a Single Forward Pass](https://mlsanity.com/paper/2402.09769/) (2024)

## Abstract (arXiv)

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.