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A deep learning framework for neuroscience

Richards, BA; Lillicrap, TP; Beaudoin, P; Bengio, Y; Bogacz, R; Christensen, A; Clopath, C; ... Kording, KP; + view all (2019) A deep learning framework for neuroscience. Nature Neuroscience , 22 (11) pp. 1761-1770. 10.1038/s41593-019-0520-2. Green open access

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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.

Type: Article
Title: A deep learning framework for neuroscience
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41593-019-0520-2
Publisher version: https://doi.org/10.1038/s41593-019-0520-2
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Learning algorithms, Machine learning, Neural circuits
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10086844
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