We could just as well probe the real system and analyze the activity of its nodes directly. PDP programs at this level become practical machines for solving computational problems, but not models in the scientific sense. Any model that behaves like this is of course useless for explaining how human cognition really works. A few models combine distributed processing with instantiation of known algorithms in a homeomorphic architecture. The approach begins with known features of neural architecture, and investigates the architecture's processing capabilities.
Lateral inhibition is ubiquitous in sensory systems.
The model is based on the Hartline-Ratliff equation for lateral inhibition, originally developed for the Limulus eye. This equation is applied iteratively for the influence of each neuron in the net on every other neuron. In practice, inhibitory coefficients k are set to 0 for neurons that are more than 3 neurons distant from neuron p in a 1-dimensional network. Thus each neuron is inhibited by its 3 nearest neighbors on either side. The output of the modeled network is an array of activations of simulated neurons.