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Computational Neuroethology: A Call to Action.
Datta, Sandeep Robert; Anderson, David J; Branson, Kristin; Perona, Pietro; Leifer, Andrew.
Affiliation
  • Datta SR; Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: srdatta@hms.harvard.edu.
  • Anderson DJ; Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Pasadena, CA, 91125, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA.
  • Branson K; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
  • Perona P; Division of Engineering & Applied Sciences 136-93, California Institute of Technology, Pasadena, CA 91125, USA.
  • Leifer A; Department of Physics, Princeton University, Princeton, NJ 08544, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA. Electronic address: leifer@princeton.edu.
Neuron ; 104(1): 11-24, 2019 10 09.
Article in En | MEDLINE | ID: mdl-31600508
ABSTRACT
The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Behavior, Animal / Brain / Machine Learning Limits: Animals Language: En Year: 2019 Type: Article

Full text: 1 Database: MEDLINE Main subject: Behavior, Animal / Brain / Machine Learning Limits: Animals Language: En Year: 2019 Type: Article