Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)
Alexandre Pouget, Kechen Zhang
Coarse codes are widely used throughout the brain to encode sen(cid:173) sory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient, i.e., the variance of the estimate is much larger than the smallest possi(cid:173) ble variance, or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a simi(cid:173) lar estimation problem . They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a non-linear recurrent network can be used to per(cid:173) form these estimation in an optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connec(cid:173) tions in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.