Institution
York University
Education•Toronto, Ontario, Canada•
About: York University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Politics. The organization has 18899 authors who have published 43357 publications receiving 1568560 citations.
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TL;DR: In this article, the authors present and estimate an empirical model of renewable energy consumption for the G7 countries and show that in the long term, increases in real GDP per capita and CO2 per capita are major drivers behind per capita renewable energy usage.
640 citations
TL;DR: This paper reviews the development and validation of the alexithymia construct and discusses its clinical implications for psychosomatic medicine.
639 citations
TL;DR: The main aim of this study was to find out the age at which people with diabetes develop a high risk of CVD, as defined by an event rate equivalent to a 10-year risk of 20% or more; or an event rates equivalent to that associated with previous myocardial infarction.
635 citations
634 citations
University College London1, Université de Montréal2, Canadian Institute for Advanced Research3, University of Oxford4, Stanford University5, Imperial College London6, University of Bern7, University of Bristol8, Google9, University of Toronto10, Cold Spring Harbor Laboratory11, Columbia University12, University of Ottawa13, McGill University14, Foundation for Research & Technology – Hellas15, Netherlands Institute for Neuroscience16, University of Zurich17, University of Pennsylvania18, Friedrich Miescher Institute for Biomedical Research19, York University20
TL;DR: It is argued that a deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation.
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.
633 citations
Authors
Showing all 19301 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Dan R. Littman | 157 | 426 | 107164 |
| Martin J. Blaser | 147 | 820 | 104104 |
| Aaron Dominguez | 147 | 1968 | 113224 |
| Gregory R Snow | 147 | 1704 | 115677 |
| Joseph E. LeDoux | 139 | 478 | 91500 |
| Kenneth Bloom | 138 | 1958 | 110129 |
| Osamu Jinnouchi | 135 | 885 | 86104 |
| Steven A. Narod | 134 | 970 | 84638 |
| David H. Barlow | 133 | 786 | 72730 |
| Elliott Cheu | 133 | 1219 | 91305 |
| Roger Moore | 132 | 1677 | 98402 |
| Wendy Taylor | 131 | 1252 | 89457 |
| Stephen P. Jackson | 131 | 372 | 76148 |
| Flera Rizatdinova | 130 | 1242 | 89525 |
| Sudhir Malik | 130 | 1669 | 98522 |