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

TL;DR

This paper presents a biologically plausible model of saccadic eye control that employs a Bayesian Kalman filter to optimally integrate proprioceptive and efference copy signals, explaining eye movement control in the posterior parietal cortex.

Contribution

It introduces a novel, biologically realistic implementation of a Bayesian Kalman filter for eye control, reconciling different data interpretations about parietal cortex functions.

Findings

The model achieves Bayes optimal eye position estimation.

It accounts for latency in proprioceptive signals.

Supports biological plausibility of Bayesian brain hypothesis.

Abstract

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.