The neuronal implementation of representational geometry in primate prefrontal cortex

Modern neuroscience has seen the rise of a population-doctrine that
represents cognitive variables using geometrical structures in activity
space. Representational geometry does not, however, account for how
individual neurons implement these representations. Here, leveraging the
principle of sparse coding, we present a framework to dissect
representational geometry into biologically interpretable components that
retain links to single neurons. Applied to extracellular recordings from
the primate prefrontal cortex in a working memory task with interference,
the identified components revealed disentangled and sequential memory
representations including the recovery of memory content after
distraction, signals hidden to conventional analyses. Remarkably, each
component was contributed by small subpopulations of neurons with distinct
electrophysiological properties and response dynamics. Modelling showed
that such sparse implementations are supported by recurrently connected
circuits as in prefrontal cortex. The perspective of neuronal
implementation links representational geometries to their cellular
constituents, providing mechanistic insights into how neural systems
encode and process information.