Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks.

ABSTRACT: One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not on

SUBMITTER: Bae H 

PROVIDER: S-EPMC7843526 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature