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Learning of biased representations in LIP through interactions between recurrent connectivity and Hebbian plasticity

Authors :
Jacqueline Gottlieb
Wujie Zhang
Kenneth D. Miller
Publication Year :
2021
Publisher :
Cold Spring Harbor Laboratory, 2021.

Abstract

SummaryWhen monkeys learn to group visual stimuli into arbitrary categories, lateral intraparietal area (LIP) neurons become category-selective. Surprisingly, the representations of learned categories are overwhelmingly biased: nearly all LIP neurons in a given animal prefer the same category over other behaviorally equivalent categories. We propose a model where such biased representations develop through the interplay between Hebbian plasticity and the recurrent connectivity of LIP. In this model, two separable processes of positive feedback unfold in parallel: in one, category selectivity emerges from competition between prefrontal inputs; in the other, bias develops due to lateral interactions among LIP neurons. This model reproduces the levels of category selectivity and bias observed under a variety of conditions, as well as the redevelopment of bias after monkeys learn redefined categories. It predicts that LIP receptive fields would spatially cluster by preferred category, which we experimentally confirm. In summary, our model reveals a mechanism by which LIP learns abstract representations and assigns meaning to sensory inputs.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........a24da5c4ea14e4e064f7867be470c57f
Full Text :
https://doi.org/10.1101/2021.09.23.461557