Kay M. Tye, Earl K. Miller, et al.
Neuron
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.
Kay M. Tye, Earl K. Miller, et al.
Neuron
Inkit Padhi, Yair Schiff, et al.
ICASSP 2021
Stefano Recanatesi, Matthew Farrell, et al.
Nature Communications
Anna Choromanska, Benjamin Cowen, et al.
ICML 2019