Biomimetic Construction of Category Mental Imagery Based on Recognition Mechanism of Visual Cortex of Human Brain

Abstract

The principle of homology-continuity in Multi-Dimensional Biomimetic Informatics Space is applied to construct the identifying mechanism of category of deep representation of mental imagery. The model of each cerebral region involved in recognizing is established respectively and a feedforward method for establishing category mental imagery is proposed. First, the model of feature acquisition is developed based on Hubel-Wiesel model, and Gaussian function is used to simulate the simple cell receptive field to satisfy the specific function of visual cortex. Second, multiple input aggregation operation is employed to simulate the feature output of complex cells to get the invariance representation in feature space. Then, imagery basis is extracted by unsupervised learning algorithm based on the primary feature and category mental imagery is obtained by building Radial Basis Function (RBF) network. Finally, the system model is tested by training set and test set composed of real images. Experimental results show that the proposed method can establish valid deep representation of these samples, based on which the biomimetic construction of category mental imagery can be achieved. This method provides a new idea for solving imagery problem and studying imagery thinking.