A deep learning framework for neuroscience
| Autor: | Panayiota Poirazi, Greg Wayne, Christopher C. Pack, Surya Ganguli, Joel Zylberberg, Pieter R. Roelfsema, Grace W. Lindsay, Blake A. Richards, Walter Senn, Colleen J Gillon, Denis Therien, Philippe Beaudoin, Anna C. Schapiro, Kenneth D. Miller, Archy O. de Berker, Yoshua Bengio, Claudia Clopath, Peter E. Latham, Amelia J. Christensen, João Sacramento, Nikolaus Kriegeskorte, Timothy P. Lillicrap, Rui Ponte Costa, Danijar Hafner, Daniel L. K. Yamins, Benjamin Scellier, Rafal Bogacz, Adam Kepecs, Richard Naud, Friedemann Zenke, Konrad P. Kording, Andrew M. Saxe |
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| Přispěvatelé: | Netherlands Institute for Neuroscience (NIN), University of Zurich, Richards, Blake A, Wellcome Trust, Biotechnology and Biological Sciences Research Council (BBSRC), Biotechnology and Biological Sciences Research Cou, Simons Foundation, National Institutes of Health |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science 1702 Cognitive Sciences media_common.quotation_subject Article 03 medical and health sciences Deep Learning 0302 clinical medicine Artificial Intelligence Perception Biological neural network Animals Humans 610 Medicine & health 10194 Institute of Neuroinformatics media_common Systems neuroscience Neurology & Neurosurgery Artificial neural network Quantitative Biology::Neurons and Cognition business.industry General Neuroscience Deep learning Perspective (graphical) Brain 2800 General Neuroscience Cognition Variety (cybernetics) 030104 developmental biology 1701 Psychology 570 Life sciences biology Neural Networks Computer Artificial intelligence 1109 Neurosciences business Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | 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 2019, ' A deep learning framework for neuroscience ', Nature Neuroscience, vol. 22, no. 11, pp. 1761-1770 . https://doi.org/10.1038/s41593-019-0520-2 Nature Neuroscience, 22(11), 1761-1770. Nature Publishing Group Nat Neurosci |
| ISSN: | 1097-6256 |
| Popis: | 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 the case of artificial neural networks, the three components specified by design are the objective functions, the learning rules, and 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. |
| Databáze: | OpenAIRE |
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