Pietro Mazzoni

Evidence for the lateral intraparietal area as the parietal eye field

It has long been appreciated that the posterior parietal cortex plays a role in the processing of saccadic eye movements. Only recently has it been discovered that a small cortical area, the lateral intraparietal area, within this much larger area appears to be specialized for saccadic eye movements. Unlike other cortical areas in the posterior parietal cortex, the lateral intraparietal area has strong anatomical connections to other saccade centers, and its cells have saccade-related responses that begin before the saccades. The lateral intraparietal area appears to be neither a strictly visual nor strictly motor structure; rather it performs visuomotor integration functions including determining the spatial location of saccade targets and forming plans to make eye movements.

A R-P learning applied to a network model of cortical area 7a

A neural network is described that learns to transform retinotopic coordinates of visual stimuli into a head-centers reference frame by combining retinal stimuli with eye position. Area 7a of the primate cortex is thought to perform a similar transformation. The neurons involved have unique response properties (planar modification of visual response by eye position and large complex receptive fields) and appear to represent head-centered space in a distributed fashion. The algorithm is a variant of the associative reward-penalty (AR-P) learning rule and uses a global reinforcement signal to adjust the connection strengths. The network learns to perform the task successfully to any accuracy and generalizes appropriately, and the hidden units develop response properties very similar to those of area 7a neurons. These results show that a learning network does not require backpropagation to acquire biologically interesting properties. These may arise naturally from the networks layered architecture and from the supervised learning paradigm

The role of neural networks in the study of the posterior parietal cortex

The use of a neural network model of a cerebral cortical area as an aid to understanding this areas function is reviewed. The basic model is a feedforward multilayer network that learns to transform the coordinates of a visual stimulus from a retinocentric to a craniocentric reference frame using backpropagation. An extension of the model to one that transforms retinal coordinates into body-centered ones predicts response properties that are confirmed by neurophysiological experiments. The simulation of electrical stimulation of the model predicts a pattern of effects similar to the one obtained by stimulation of a specific region of the parietal cortex. The study of the response properties of the models units provides a simple explanation of how the parietal cortex might compute coordinate transformations and of why certain manipulations such as stimulation should produce the effects observed.<<ETX>>