Neko: a Library for Exploring Neuromorphic Learning Rules

TL;DR

Neko is an open-source Python library designed to facilitate the development and testing of neuromorphic learning rules, demonstrating versatility and performance improvements in various learning scenarios.

Contribution

It introduces a modular, extensible library for neuromorphic learning rules, filling a gap in software tools for designing new algorithms.

Findings

Neko can replicate state-of-the-art algorithms.

It achieves significant accuracy and speed improvements in some cases.

Provides tools like gradient comparison for algorithm development.

Abstract

The field of neuromorphic computing is in a period of active exploration. While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking models, general software libraries for learning rules remain underexplored. This is partly due to the diverse, challenging nature of efforts to design new learning rules, which range from encoding methods to gradient approximations, from population approaches that mimic the Bayesian brain to constrained learning algorithms deployed on memristor crossbars. To address this gap, we present Neko, a modular, extensible library with a focus on aiding the design of new learning algorithms. We demonstrate the utility of Neko in three exemplar cases: online local learning, probabilistic learning, and analog on-device learning. Our results show that Neko can replicate the state-of-the-art algorithms and, in one case, lead to significant outperformance in accuracy and speed. Further, it offers tools including gradient comparison that can help develop new algorithmic variants. Neko is an open source Python library that supports PyTorch and TensorFlow backends.