Artificial neural networks for neuroscientists: A primer

TL;DR

This paper introduces artificial neural networks to neuroscientists, explaining their concepts, methods, and applications in brain research, with practical tutorials to facilitate understanding and implementation.

Contribution

It provides a comprehensive, accessible primer on ANNs tailored for neuroscientists, including customization techniques and hands-on tutorials.

Findings

Demonstrates how ANNs can model complex neural behaviors

Shows customization of ANNs for neurobiological data analysis

Provides practical tutorials for implementation in PyTorch

Abstract

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help the readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.