Mouse prefrontal cortex represents learned rules for categorization

Reinert, Sandra; Hübener, Mark; Bonhoeffer, Tobias und Goltstein, Pieter M. (2021): Mouse prefrontal cortex represents learned rules for categorization. In: Nature, Bd. 593, Nr. 7859: 411-

Abstract

The ability to categorize sensory stimuli is crucial for an animal's survival in a complex environment. Memorizing categories instead of individual exemplars enables greater behavioural flexibility and is computationally advantageous. Neurons that show category selectivity have been found in several areas of the mammalian neocortex(1-4), but the prefrontal cortex seems to have a prominent role(4,5) in this context. Specifically, in primates that are extensively trained on a categorization task, neurons in the prefrontal cortex rapidly and flexibly represent learned categories(6,7). However, how these representations first emerge in naive animals remains unexplored, leaving it unclear whether flexible representations are gradually built up as part of semantic memory or assigned more or less instantly during task execution(8,9). Here we investigate the formation of a neuronal category representation throughout the entire learning process by repeatedly imaging individual cells in the mouse medial prefrontal cortex. We show that mice readily learn rule-based categorization and generalize to novel stimuli. Over the course of learning, neurons in the prefrontal cortex display distinct dynamics in acquiring category selectivity and are differentially engaged during a later switch in rules. A subset of neurons selectively and uniquely respond to categories and reflect generalization behaviour. Thus, a category representation in the mouse prefrontal cortex is gradually acquired during learning rather than recruited ad hoc. This gradual process suggests that neurons in the medial prefrontal cortex are part of a specific semantic memory for visual categories.

• Dokumententyp: Zeitschriftenartikel
• Fakultätsübergreifende Einrichtungen: Graduate School of Systemic Neurosciences (GSN)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
• ISSN: 0028-0836
• Sprache: Englisch