  The human visual system contains a hierarchical sequence of modules that take
part in visual perception at superordinate, basic, and subordinate
categorization levels. During the last decades, various computational models
have been proposed to mimic the hierarchical feed-forward processing of visual
cortex, but many critical characteristics of the visual system, such actual
neural processing and learning mechanisms, are ignored. Pursuing the line of
biological inspiration, we propose a computational model for object recognition
in different categorization levels, in which a spiking neural network equipped
with the reinforcement learning rule is used as a module at each categorization
level. Each module solves the object recognition problem at each categorization
level, solely based on the earliest spike of class-specific neurons at its last
layer, without using any external classifier. According to the required
information at each categorization level, the relevant band-pass filtered
images are utilized. The performance of our proposed model is evaluated by
various appraisal criteria with three benchmark datasets and significant
improvement in recognition accuracy of our proposed model is achieved in all
experiments.
