# NeuroPack: An Algorithm-level Python-based Simulator for Memristor-empowered Neuro-inspired Computing

arXiv: https://arxiv.org/abs/2201.03339 · HTML: https://mlsanity.com/paper/2201.03339/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2022.

## Built on by (most cited first, 2 of 2)

- [Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays](https://mlsanity.com/paper/2206.12992/) (2022)
- [Non-frontal face recognition using GANs and memristor-based classifiers](https://mlsanity.com/paper/2606.12074/) (2026)

## Abstract (arXiv)

Emerging two terminal nanoscale memory devices, known as memristors, have over the past decade demonstrated great potential for implementing energy efficient neuro-inspired computing architectures. As a result, a wide-range of technologies have been developed that in turn are described via distinct empirical models. This diversity of technologies requires the establishment of versatile tools that can enable designers to translate memristors' attributes in novel neuro-inspired topologies. In this paper, we present NeuroPack, a modular, algorithm level Python-based simulation platform that can support studies of memristor neuro-inspired architectures for performing online learning or offline classification. The NeuroPack environment is designed with versatility being central, allowing the user to chose from a variety of neuron models, learning rules and memristors models. Its hierarchical structure, empowers NeuroPack to predict any memristor state changes and the corresponding neural network behavior across a variety of design decisions and user parameters options. The use of NeuroPack is demonstrated herein via an application example of performing handwritten digit classification with the MNIST dataset and an existing empirical model for metal-oxide memristors.