Efficient computation and cue integration with noisy population codes.

@article{citeulike:90461, abstract = {The brain represents sensory and motor variables through the activity of large populations of neurons. It is not understood how the nervous system computes with these population codes, given that individual neurons are noisy and thus unreliable. We focus here on two general types of computation, function approximation and cue integration, as these are powerful enough to handle a range of tasks, including sensorimotor transformations, feature extraction in sensory systems and multisensory integration. We demonstrate that a particular class of neural networks, basis function networks with multidimensional attractors, can perform both types of computation optimally with noisy neurons. Moreover, neurons in the intermediate layers of our model show response properties similar to those observed in several multimodal cortical areas. Thus, basis function networks with multidimensional attractors may be used by the brain to compute efficiently with population codes.}, address = {Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627, USA.}, author = {Deneve, S. and Latham, P. E. and Pouget, A. }, citeulike-article-id = {90461}, doi = {10.1038/90541}, issn = {1097-6256}, journal = {Nat Neurosci}, keywords = {computational, neurobiology}, month = {August}, number = {8}, pages = {826--831}, posted-at = {2006-01-31 14:50:16}, priority = {2}, title = {Efficient computation and cue integration with noisy population codes.}, url = {http://dx.doi.org/10.1038/90541}, volume = {4}, year = {2001} }

TY - JOUR ID - citeulike:90461 L3 - citeulike-article-id:90461 TI - Efficient computation and cue integration with noisy population codes. JF - Nat Neurosci VL - 4 IS - 8 SP - 826 EP - 831 AD - Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627, USA. SN - 1097-6256 N2 - The brain represents sensory and motor variables through the activity of large populations of neurons. It is not understood how the nervous system computes with these population codes, given that individual neurons are noisy and thus unreliable. We focus here on two general types of computation, function approximation and cue integration, as these are powerful enough to handle a range of tasks, including sensorimotor transformations, feature extraction in sensory systems and multisensory integration. We demonstrate that a particular class of neural networks, basis function networks with multidimensional attractors, can perform both types of computation optimally with noisy neurons. Moreover, neurons in the intermediate layers of our model show response properties similar to those observed in several multimodal cortical areas. Thus, basis function networks with multidimensional attractors may be used by the brain to compute efficiently with population codes. KW - computational KW - neurobiology AU - Deneve, S AU - Latham, PE AU - Pouget, A PY - 2001/08// UR - http://dx.doi.org/10.1038/90541 ER -