Neural network models and deep learning - a primer for biologists

TL;DR

This paper provides an accessible introduction to neural network models and deep learning tailored for biologists, explaining core concepts, algorithms, and potential insights into brain function.

Contribution

It offers a clear overview of neural network architectures and learning algorithms, bridging machine learning and neuroscience for biological researchers.

Findings

Neural networks can approximate complex biological functions.

Backpropagation enables effective training of deep models.

Deep learning offers insights into brain computations.

Abstract

Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence, where they are used to approximate functions and dynamics by learning from examples. Here we give a brief introduction to neural network models and deep learning for biologists. We introduce feedforward and recurrent networks and explain the expressive power of this modeling framework and the backpropagation algorithm for setting the parameters. Finally, we consider how deep neural networks might help us understand the brain's computations.