  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.
