Identified circuit in rat postrhinal cortex encodes essential information for performing specific visual shape discriminations.

Abstract:

:Learning theories hypothesize specific circuits encode essential information for performance. For simple tasks in invertebrates and mammals, the essential circuits are known, but for cognitive functions, the essential circuits remain unidentified. Here, we show that some essential information for performing a choice task is encoded in a specific circuit in a neocortical area. Rat postrhinal (POR) cortex is required for visual shape discriminations, protein kinase C (PKC) pathways mediate changes in neuronal physiology that support learning, and specific PKC genes are required for multiple learning tasks. We used direct gene transfer of a constitutively active PKC to prime a specific POR cortex circuit for learning visual shape discriminations. In the experiment, rats learned a discrimination, received gene transfer, learned new discriminations, received a small lesion that ablated approximately 21% of POR cortex surrounding the gene transfer site, and were tested for performance for discriminations learned either before or after gene transfer. Lesions of the genetically targeted circuit selectively interfered with performance for discriminations learned after gene transfer. Activity-dependent gene imaging confirmed increased activity in the genetically targeted circuit during learning and showed the essential information was sparse-coded in approximately 500 neurons in the lesioned area. Wild-type rats contained circuits with similar increases in activity during learning, but these circuits were located at unpredictable, different positions in POR cortex. These results establish that some essential information for performing specific visual discriminations can be encoded in a small, identified, neocortical circuit and provide a foundation for characterizing the circuit and essential information.

authors

Zhang GR,Cao H,Kong L,O'Brien J,Baughns A,Jan M,Zhao H,Wang X,Lu XG,Cook RG,Geller AI

doi

10.1073/pnas.0912950107

subject

Has Abstract

pub_date

2010-08-10 00:00:00

pages

14478-83

issue

32

eissn

0027-8424

issn

1091-6490

pii

0912950107

journal_volume

107

pub_type

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