Interpreting wide-band neural activity using convolutional neural networks

eLife

Sander Tanni¹  Julie Lefort¹  Caswell Barry¹  Alice O'Leary¹  Andrea Banino²  Catherine Perrodin³  Daniel Bendor³  Markus Frey⁴  Matthias Nau⁴  Christian F Doeller⁵  Jack Kelly⁶ 

[1] Cell & Developmental Biology, UCL, London, United Kingdom;DeepMind, London, United Kingdom;Institute of Behavioural Neuroscience, UCL, London, United Kingdom;Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, NTNU, Norwegian University of Science and Technology, Trondheim, Norway;Max-Planck-Insitute for Human Cognitive and Brain Sciences, Leipzig, Germany;Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, NTNU, Norwegian University of Science and Technology, Trondheim, Norway;Max-Planck-Insitute for Human Cognitive and Brain Sciences, Leipzig, Germany;Institute of Psychology, Leipzig University, Leipzig, Germany;Open Climate Fix, London, United Kingdom;

关键词: deep learning;    decoding;    calcium imaging;    electrophysiology;    Rat;   

DOI  :  10.7554/eLife.66551

来源: eLife Sciences Publications, Ltd

【 摘 要 】

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 requires considerable knowledge about the nature of the representation and often depends on manual operations. 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 behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity.

【 授权许可】

CC BY