Neuroscience Needs Behavior: Correcting a Reductionist Bias
John W. Krakauer,Asif A. Ghazanfar,Alex Gomez-Marin,Malcolm A. MacIver,David Poeppel,David Poeppel +5 more
TLDR
A more pluralistic notion of neuroscience is advocated when it comes to the brain-behavior relationship: behavioral work provides understanding, whereas neural interventions test causality.About:
This article is published in Neuron.The article was published on 2017-02-08 and is currently open access. It has received 920 citations till now.read more
Citations
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DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Alexander Mathis,Pranav Mamidanna,Kevin M. Cury,Taiga Abe,Venkatesh N. Murthy,Mackenzie W. Mathis,Mackenzie W. Mathis,Matthias Bethge +7 more
TL;DR: Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.
Journal ArticleDOI
Neuroscience-Inspired Artificial Intelligence.
TL;DR: It is argued that better understanding biological brains could play a vital role in building intelligent machines in humans and other animals.
Journal ArticleDOI
A deep learning framework for neuroscience
Blake A. Richards,Timothy P. Lillicrap,Philippe Beaudoin,Yoshua Bengio,Yoshua Bengio,Rafal Bogacz,Amelia J. Christensen,Claudia Clopath,Rui Ponte Costa,Rui Ponte Costa,Archy O. de Berker,Surya Ganguli,Surya Ganguli,Colleen J Gillon,Danijar Hafner,Danijar Hafner,Adam Kepecs,Nikolaus Kriegeskorte,Peter E. Latham,Grace W. Lindsay,Kenneth D. Miller,Richard Naud,Christopher C. Pack,Panayiota Poirazi,Pieter R. Roelfsema,João Sacramento,Andrew M. Saxe,Benjamin Scellier,Anna C. Schapiro,Walter Senn,Greg Wayne,Daniel L. K. Yamins,Friedemann Zenke,Friedemann Zenke,Joel Zylberberg,Joel Zylberberg,Denis Therien,Konrad P. Kording,Konrad P. Kording +38 more
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.
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Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.
Max Jaderberg,Wojciech Marian Czarnecki,Iain Dunning,Luke Marris,Guy Lever,Antonio García Castañeda,Charles Beattie,Neil C. Rabinowitz,Ari S. Morcos,Avraham Ruderman,Nicolas Sonnerat,Tim Green,Louise Deason,Joel Z. Leibo,David Silver,Demis Hassabis,Koray Kavukcuoglu,Thore Graepel +17 more
TL;DR: In this article, the authors demonstrate that an agent can achieve human-level performance in a popular 3D multiplayer first-person video game, Quake III Arena Capture the Flag, using only pixels and game points as input.
References
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