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The posterior parietal cortex is believed to direct eye movements, especiallyin regards to target tracking tasks, and a number of debates exist over theprecise nature of the computations performed by the parietal cortex, with eachside supported by different sets of biological evidence. In this paper I willpresent my model which navigates a course between some of these debates,towards the end of presenting a model which can explain some of the competinginterpretations among the data sets. In particular, rather than assuming thatproprioception or efference copies form the key source of information forcomputing eye position information, I use a biological plausible implementationof a Kalman filter to optimally combine the two signals, and a simple gaincontrol mechanism in order to accommodate the latency of the proprioceptivesignal. Fitting within the Bayesian brain hypothesis, the result is a Bayesoptimal solution to the eye control problem, with a range of data supportingclaims of biological plausibility.

A Biologically Realistic Model of Saccadic Eye Control with Probabilistic Population Codes