Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex

Francesco Ceccarelli, Lorenzo Ferrucci, Fabrizio Londei, Surabhi Ramawat, Emiliano Brunamonti and Aldo Genovesio ()
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Francesco Ceccarelli: Sapienza University
Lorenzo Ferrucci: Sapienza University
Fabrizio Londei: Sapienza University
Surabhi Ramawat: Sapienza University
Emiliano Brunamonti: Sapienza University
Aldo Genovesio: Sapienza University

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.

Date: 2023
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DOI: 10.1038/s41467-023-43712-2

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