A Bayesian method for estimating orientation map of visual cortex

Abstract

The combined use of intrinsic optical imaging and electrophysiological recording has become an important method to reveal the fine-scale structure of orientation map in the primary visual cortex. However, it often needs many repetitions to obtain the mean activity as a result of the low signal-to-noise ratio of intrinsic optical imaging. To overcome this problem, we proposed a Bayesian method to obtain the highly accurate orientation map with less repetitions by fusing the intrinsic optical imaging and electrophysiological recording. We first used a Gaussian regression model to obtain the posterior distribution of the cortical orientation map with the intrinsic optical imaging data. And then we computed the conditional distribution of orientation map given the measurements from electrophysiological recording. The simulation results suggested that our method had significant improvement of performance compared with the classical methods and was very robust to noise.