Why neurons mix: high dimensionality for higher cognition

Stefano Fusi, Earl K Miller, Mattia Rigotti
2016 Current Opinion in Neurobiology  
Neurons often respond to diverse combinations of taskrelevant 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
more » ... 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. Addresses
doi:10.1016/j.conb.2016.01.010 pmid:26851755 fatcat:dk6cq44lgreyfhoeljplm7k5bq