The input layer

The input layer is composed of MT nodes each responding preferentially to a local translational motion of a particular direction. The visual area under consideration can be considered as being composed of $k \times k$ patches of the same size as the receptive field of the MT neurons. Each patch is represented by 8 MT nodes of 8 different preferred motion directions. These MT nodes are binary and they are usually off unless a local motion is detected in the patch, in which case one of the 8 nodes whose preferred direction is closest to the motion direction is turned on. For simplicity, only the direction of each vector of the flow field was used in the model to represent the motion. The magnitude of the vector was ignored. Among all possible patterns that may be formed in the k by k array of vectors, we are only interested in those that represent optic flow patterns such as the different types of motions shown in Fig. . There are in total 8+8k² input patterns including 8 translations of different directions, and 8 radial, circular and spiral motion patterns each of k² COM locations.

Similar to the MT model, here again we need to account for the noises of different types in the real image, caused by missing or false local motion information, due to either homogeneous luminance, or the aperture problem. These noises are simulated the same way as in the MT model case.