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Poster C29 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
Segregated neuronal populations in prefrontal cortex encode task variables during working memory
Klaus Wimmer1, Bijan Pesaran2, Nicolas Pollan1; 1Centre de Recerca Matemàtica, Barcelona, Spain, 2University of Pennsylvania, PA, USA
Mixed selectivity, with neurons responding to diverse combinations of task-relevant variables, has been proposed as a key mechanism to enable flexible behavior and cognition. However, it is debated whether neural population responses in prefrontal cortex are better described as random mixed-selective or as non-random, that is, in terms of multiple subpopulations with stereotypical response profiles. Here, we show that neural activity in macaque prefrontal cortex during a working memory task is organized into subpopulations that provide a comprehensive description of the low-dimensional population dynamics. Using demixed-PCA and model-free clustering, we find that stimulus identity, task condition and elapsed time are encoded in the population activity with a significant degree of clustering, incompatible with random mixed selectivity. Examining the contribution of stimulus-selective neurons to task condition-related variance reveals two contrasting activity profiles that correspond to functionally different populations, one responding during visual stimulation and the other one during memory maintenance. Finally, the observed neural geometry explains how stable task and stimulus information can be read out from the population response. Our results highlight that despite the heterogeneity of prefrontal responses during working memory, neurons do not represent random mixtures of task features but are structured according to neural subpopulations.
Keywords: working memory prefrontal cortex neural population code