RT Journal Article
SR Electronic
T1 Interpreting Wide-Band Neural Activity Using Convolutional Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 871848
DO 10.1101/871848
A1 Markus Frey
A1 Sander Tanni
A1 Catherine Perrodin
A1 Alice O’Leary
A1 Matthias Nau
A1 Jack Kelly
A1 Andrea Banino
A1 Daniel Bendor
A1 Christian F. Doeller
A1 Caswell Barry
YR 2020
UL http://biorxiv.org/content/early/2020/11/26/871848.abstract
AB Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by putative CA1 interneurons.Competing Interest StatementThe authors have declared no competing interest.