Electrophysiological recordings of prefrontal activity over learning in non-human primates

The relationship between the geometry of neural representations and the
task being performed is a central question in neuroscience. The primate
prefrontal cortex (PFC) is a primary focus of inquiry in this regard, as
under different conditions, PFC can encode information with geometries
that either rely on past experience or are experience agnostic. One
hypothesis is that PFC representations should evolve with learning, from a
format that supports exploration of all possible task rules to a format
that minimises metabolic cost and supports generalisation. Here we test
this idea by recording neural activity from PFC when learning a new rule
(‘XOR rule’) from scratch. We show that PFC representations progress from
being high dimensional and randomly mixed to low dimensional and rule
selective, consistent with predictions from metabolically constrained
optimised neural networks. We also find that this low-dimensional
representation facilitates generalisation of the XOR rule to a new
stimulus set. These results show that previously conflicting accounts of
PFC representations can be reconciled by considering the adaptation of
these representations across learning.