Recoding arm position to learn visuomotor transformations

There is strong experimental evidence that guiding the arm toward a visual target involves an initial vectorial transformation from direction in visua l space to direction in motor space. Constraints on this transformation are imposed (i) by the neural codes for incoming information: the desired move ment direction is thought to be signalled by populations of broadly tuned n eurons and arm position by populations of monotonically tuned neurons; and (ii) by the properties of outgoing information: the actual movement directi on results from the collective action of broadly tuned neurons whose prefer red directions rotate with the position of the arm. A neural network model is presented that computes the visuomotor mapping, given these constraints. Appropriate operations are learned by the network in an unsupervised fashi on through repeated action-perception cycles by recoding the arm-related pr oprioceptive information. The resulting solution has two interesting proper ties: (i) the required transformation is executed accurately over a large p art of the reaching space, although few positions are actually learned; and (ii) properties of single neurons and populations in the network closely r esemble those of neurons and populations in parietal and motor cortical reg ions. This model thus suggests a realistic scenario for the calculation of coordinate transformations and initial motor command for arm reaching movem ents.