The output from the retinal layer feeds into the model LGN, which is described by a feed-forward competitive shunting network (
Figure 12). Shunting inhibition is used to assure that the network remains normalized throughout a range of possible input intensities. The LGN is analytically described by:
where
I is an input vector,
x is the associated output activation vector, n is the number of units in both the input and output layers,
t is time, B and D are shunting parameters, and
C_{ki} and
E_{ki} are Gaussian kernels. Note that the decay-rate parameter
A_{i}, the rise-time parameter
τ_{i}, and the Gaussian functions
C_{ki} and
E_{ki} are all a function of eccentricity.
C_{ki} and
E_{ki} are defined as:
where
μ and
ν are the variances of the Gaussians, (
k–i) is the distance from the center of the receptive field centroid of node
i to node
k, and
C and
E are parameters. For the eccentricity-dependent parameters
A_{i} and
τ_{i} we tried many different functional forms and find that simple linear functions still allow enough flexibility in the model to accurately fit both the physiological and psychophysical data.
where the individual parameter values can be found in
Table 2.