Task-Related Synaptic Changes Localized to Small Neuronal Population in Recurrent Neural Network Cortical Models

Humans have flexible control over cognitive functions depending on the context. Several studies suggest that the prefrontal cortex (PFC) controls this cognitive flexibility, but the detailed underlying mechanisms remain unclear. Recent developments in machine learning techniques allow simple PFC models written as a recurrent neural network to perform various behavioral tasks like humans and animals. Computational modeling allows the estimation of neuronal parameters that are crucial for performing the tasks, which cannot be observed by biologic experiments. To identify salient neural-network features for flexible cognition tasks, we compared four PFC models using a context-dependent integration task. After training the neural networks with the task, we observed highly plastic synapses localized to a small neuronal population in all models. In three of the models, the neuronal units containing these highly plastic synapses contributed most to the performance. No common tendencies were observed in the distribution of synaptic strengths among the four models. These results suggest that task-dependent plastic synaptic changes are more important for accomplishing flexible cognitive tasks than the structures of the constructed synaptic networks.