Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing.
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.
Current high-resolution imaging techniques require an intact sample that preserves spatial relationships.
no code implementations • 24 Jun 2013 • Adam H. Marblestone, Bradley M. Zamft, Yael G. Maguire, Mikhail G. Shapiro, Thaddeus R. Cybulski, Joshua I. Glaser, Dario Amodei, P. Benjamin Stranges, Reza Kalhor, David A. Dalrymple, Dongjin Seo, Elad Alon, Michel M. Maharbiz, Jose M. Carmena, Jan M. Rabaey, Edward S. Boyden, George M. Church, Konrad P. Kording
Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience.
Neurons and Cognition Biological Physics