Why neurons mix: high dimensionality for higher cognition

- Mixed selectivity: neurons respond to diverse non-linear combinations of task relevant variables.
- Mixed selectivity is a signature of high dimensional neural representations.
- High dimensional neural representations enable simple readouts to generate a huge number of responses.
- Recorded neural representations are often high dimensional.

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.