A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., Gillon, C. J., Hafner, D., Kepecs, A., Kriegeskorte, N., Latham, P., Lindsay, G. W., Miller, K. D., Naud, R., Pack, C. C., Poirazi, P., Roelfsema, P., Sacramento, J., Saxe, A., Scellier, B., Schapiro, A. C., Senn, W., Wayne, G., Yamins, D., Zenke, F., Zylberberg, J., Therien, D., Kording, K. P. (November 2019) A deep learning framework for neuroscience. Nat Neurosci, 22 (11). pp. 1761-1770. ISSN 1097-6256

URL: https://www.ncbi.nlm.nih.gov/pubmed/31659335
DOI: 10.1038/s41593-019-0520-2

Abstract

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Item Type: Paper
Subjects: organism description > animal behavior > perception > cognition
bioinformatics > computational biology
bioinformatics > computational biology > algorithms > machine learning
CSHL Authors:
Communities: CSHL labs > Kepecs lab
Depositing User: Matthew Dunn
Date: November 2019
Date Deposited: 08 Nov 2019 17:07
Last Modified: 08 Nov 2019 17:07
Related URLs:
URI: https://repository.cshl.edu/id/eprint/38668

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