Temporal learning of bottom-up connections via spatially nonspecific top-down inputs

Abstract

In the brain, high-order and low-order areas are connected via bottom-up connections (from low-order to high-order areas) and top-down connections (from high-order to low-order areas). While bottom-up signals are thought to be critical in generating perception, functions of top-down signals have not been clearly delineated. One popular theory is that top-down inputs modify the activity of specific cell assemblies to modulate responses to bottom-up inputs. However, a different line of studies proposes that not all top-down inputs are specifically delivered. As the leading theories cannot account for nonspecific top-down inputs, we seek potential functions of nonspecific top-down signals using network models in our study. Our simulation results suggest that top-down inputs can regulate low-order area responses by providing temporal information even without spatial specificity. Specifically, the temporal information in nonspecific top-down inputs can weaken the undesired bottom-up connections, contributing to bottom-up connections learning. Further, we found that cortical rhythms (synchronous oscillatory neural responses) are critical in the proposed learning process of bottom-up connections in our model.