Abstract
Intrinsic optical imaging has been widely used to elucidate functional architectures in the cerebral cortex. Although several methods have been invented to visualize the functional architectures from intrinsic signals, which are very small, they are limited to extracting them because they don't take into account the tuning properties of optical signals. We propose here a new method to represent functional maps on stimulus selectivity at each local region by calculating the correlation coefficient between the optical signals and the tuning curve model. We have applied it to the visualization of orientation preference columns in the cat visual cortex. The extracted maps from the new method have a higher contrast than maps created using the conventional method, resulting in orientation preference columns that are clearly identifiable.
