Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch

• URL: http://arxiv.org/abs/2302.07624v1
• Date: Wed, 15 Feb 2023 12:35:53 GMT
• Title: Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch
• Authors: Davide Liberato Manna, Alex Vicente-Sola, Paul Kirkland, Trevor Joseph Bihl, Gaetano Di Caterina
• Abstract summary: Spiking Neural Networks (SNNs) are specifically oriented towards data science applications. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications.
• Score: 0.3425341633647624
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation. Software frameworks aid and ease this process by providing a set of ready-to-use tools that researchers can leverage. The recent interest in NM technology has seen the development of several new frameworks that do this, and that add up to the panorama of already existing libraries that belong to neuroscience fields. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. We emphasize the availability of spiking neuron models and learning rules to more easily direct decisions on the most suitable frameworks to carry out different types of research. Furthermore, we present an extension to the SpykeTorch framework that gives users access to a much broader choice of neuron models to embed in SNNs and make the code publicly available.

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