A Neural Model for Detecting and Labeling Motion Patterns 
in Image Sequences

In the present talk, I propose a neural model of the
primate motion processing hierarchy and describe its 
implementation as a computer simulation. The model aims 
to explain how a hierarchical feedforward network 
consisting of neurons in the cortical areas V1, MT, and
MST of primates achieves the detection of different 
kinds of motion patterns. Moreover, the model includes 
a feedback gating network that implements a biologically 
plausible mechanism of visual attention. This mechanism 
is used for sequential localization and fine-grained 
inspection of every motion pattern detected in the 
visual scene.