Frontiers in Computational Neuroscience Computational Neuroscience

Abstract

of overlapping tuning curves, is widely used for neural representations of various visual parameters (color, stereo depth, motion, etc.). As there were no synaptic interactions between neurons in the model (as is typical for models of population coding), the model did not form a neural network. We were primarily interested in the representation of space in visually responsive areas in occipital, parietal, inferotemporal, and prefrontal cortices, but some aspects of the approach developed here might transfer to other topics such as the construction of spatial maps for navigation in the hippocampus. The model was restricted to consideration of 2D stimuli, as neural mechanisms for the extraction of depth information include additional mechanisms than those for the other two dimensions (although the final representation of space might not differ for the three dimensions). A frontoparallel plane (similar to a computer screen) defined the universe of all possible stimulus locations serving as inputs to the model. Given this stimulus set, the model did not include consideration of any depth cues, whether binocular (stereo, disparity, or vergence angle) or monocular (texture gradients, for example), and all stimulus representations were monocular. To a limited extent, neural representation of space in the depth domain has been considered previously (Lehky and Sejnowski, 1990). In addition, we were concerned solely with modeling retinotopic space and did not examine coordinate transforms to other frames of reference.