On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach
Author(s)
Villagrasa, Francesc; Baladron, Javier; Vitay, Julien; Schroll, Henning; Antzoulatos, Evan G.; Miller, Earl K; Hamker, Fred H.; ... Show more Show less
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In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the circuit mechanisms mediating their interaction remain to be explored. We developed a novel neurocomputational model of category learning that particularly addresses the BG–PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico–thalamo–cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the corticocortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011). Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data based on the model’s predictions show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neurocomputational model therefore provides new testable insights of systems-level brain circuits involved in category learning that may also be generalized to better understand other cortico–BG–cortical loops.
Date issued
2018-09Department
Picower Institute for Learning and Memory; Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
Journal of Neuroscience
Publisher
Society for Neuroscience
Citation
Villagrasa, Francesc et al. "On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach." Journal of Neuroscience 38, 44 (October 2018): 9551-9562 © 2018 The Authors
Version: Final published version
ISSN
0270-6474
1529-2401