Task complexity and learning dynamics in prefrontal and motor structures: a neural network model

S.E. LEW, H.G. REY, S.B. ZANUTTO

EEUU

Congreso; Neuroscience 2006; 2006

Society for Neuroscience

<!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-US;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 3.0cm 70.85pt 3.0cm; mso-header-margin:35.4pt; mso-footer-margin:35.4pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> A neural network based on biological hypotheses is presented. Simulation results account for physiological and behavioral data. The model is able to learn simple operant experiments as well as complex ones as delayed matching to sample and Go/No-Go paradigms. Recently, several authors showed that neurons in the basal ganglia of monkeys show faster changes in activity than those in the prefrontal cortex during learning. This motivated the hypothesis that changes in the basal ganglia activity can “lead” those in the prefrontal cortex. Given that the prefrontal cortex is a key player in the learning of complex paradigms, we tested the former hypothesis in our neural network model. Even though the model accounted for the results in the visual discrimination task no such “leading” was observed in the Go/No-Go task. Moreover, our model predicts that learning of complex rules involves concurrently both the basal ganglia and the prefrontal cortex.