A dynamic neural network model on global-to-local interaction over time course

Abstract

We propose a neural network model based on contextual learning and non-leaky integrate-and-fire (IF) model. The model shows dynamic properties that integrate the inputs from its own module as well as the other module over time. Moreover, the integration of inputs from different modules is not simple accumulation of activation over the time course but depends on the interaction between primary input that the behaviour of a modular network should be based on, and the contextual input that facilitates or interferes with the performance of the modular network. The learning rule is derived under the assumption that time scale of the interval to first spike can be adjusted during the learning process. The model is applied to explain global-to-local processing of Navon type stimuli in which a global letter hierarchically consists of local letters. The model provides interesting insights that may underlie asymmetric response of global and local interaction found in many psychophysical and neuropsychological studies.