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Curr Opin Neurobiol. 2016 Apr;37:66-74. doi: 10.1016/j.conb.2016.01.010. Epub 2016 Feb 4.

Why neurons mix: high dimensionality for higher cognition.

Author information

1
Center for Theoretical Neuroscience, Columbia University College of Physicians and Surgeons, USA. Electronic address: sf2237@columbia.edu.
2
The Picower Institute for Learning and Memory & Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA.
3
IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA.

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.

PMID:
26851755
DOI:
10.1016/j.conb.2016.01.010
[Indexed for MEDLINE]

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