  The posterior parietal cortex is believed to direct eye movements, especially
in regards to target tracking tasks, and a number of debates exist over the
precise nature of the computations performed by the parietal cortex, with each
side supported by different sets of biological evidence. In this paper I will
present my model which navigates a course between some of these debates,
towards the end of presenting a model which can explain some of the competing
interpretations among the data sets. In particular, rather than assuming that
proprioception or efference copies form the key source of information for
computing eye position information, I use a biological plausible implementation
of a Kalman filter to optimally combine the two signals, and a simple gain
control mechanism in order to accommodate the latency of the proprioceptive
signal. Fitting within the Bayesian brain hypothesis, the result is a Bayes
optimal solution to the eye control problem, with a range of data supporting
claims of biological plausibility.

Neural and Evolutionary Computing;Neurons and Cognition