The importance of mixed selectivity in complex cognitive tasks

Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input–output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons. Neurophysiology experiments in behaving animals are often analysed and modelled with a reverse engineering perspective, with the more or less explicit intention to identify highly specialized components with distinct functional roles in the behaviour under study. Physiologists often find the components they are looking for, contributing to the understanding of the functions and the underlying mechanisms of various brain areas, but they are also bewildered by numerous observations that are difficult to interpret. Many cells, especially in higherorder brain structures like the prefrontal cortex (PFC), often have complex and diverse response properties that are not organized anatomically, and that simultaneously reflect different parameters. These neurons are said to have mixed selectivity to multiple aspects of the task. For instance, in rule-based sensory-motor mapping tasks (such as the Wisconsin card sorting test), the response of a PFC cell may be correlated with parameters of the sensory stimuli, task rule, motor response or any combination of these 1,2 . The predominance of these mixed selectivity neurons seems to be a hallmark of PFC and other brain structures involved in cognition. Understanding such neural representations has been a major conceptual challenge in the field. To characterize the statistics and understand the functional role of mixed selectivity, we analysed neural activity recorded in the PFC of monkeys during a sequence memory task 3 . Motivated by recent theoretical advances in understanding how machine learning principles play out in the functioning of neuronal circuits 4–10 , we devised a new analysis of the recorded population activity. This analysis revealed that the observed mixed selectivity can be naturally understood as a signature of the information-encoding strategy of state-of-the-art classifiers like support vector machines 11 . Specifically we found that (1) the populations of recorded neurons encode distributed information that is not contained in classical selectivity to individual task aspects, (2) the recorded neural representations are high-dimensional, and (3) the dimensionality of the recorded neural representations predicts behavioural performance.