title: Why neurons mix: high dimensionality for higher cognition.
abstract: Neurons often respond to diverse combinations of task-relevant variables. This form of mixed selectivity plays an important computational role which is related to the dimensionality of the neural representations: high-dimensional representations with mixed selectivity allow a simple linear readout to generate a huge number of different potential responses. In contrast, neural representations based on highly specialized neurons are low dimensional and they preclude a linear readout from generating several responses that depend on multiple task-relevant variables. Here we review the conceptual and theoretical framework that explains the importance of mixed selectivity and the experimental evidence that recorded neural representations are high-dimensional. We end by discussing the implications for the design of future experiments.
authors: Center for Theoretical Neuroscience, Columbia University College of Physicians and Surgeons, USA. Electronic address: sf2237@columbia.edu.
authors: Stefano
authors: S
authors: Fusi
authors: The Picower Institute for Learning and Memory & Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA.
authors: Earl K
authors: EK
authors: Miller
authors: IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA.
authors: Mattia
authors: M
authors: Rigotti
mesh_terms: D000818:Animals; D003071:Cognition / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*