Neural network models and deep learning - a primer for biologists

By Nikolaus Kriegeskorte, Tal Golan. Current Biology, 2019

465 citations recorded by the source corpus.

Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence. They can 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 network models might help us understand brain computation.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/de9a58f230750cfef201f9fd75c032c380d062cb

Citations in the corpus

• Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020, unknown): “Importantly, this review is not intended to be a thorough overview of CNNs or extensively cover all uses of deep learning in the study of vision (other reviews may be of use to the reader for this [1, 2, 3, 4, 5, 75]).”