STATISTICALLY EFFICIENT ESTIMATION USING POPULATION CODING

Coarse codes are widely used throughout the brain to encode sensory an d motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient (the variance of th e estimate is much larger than the smallest possible variance) or biol ogically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variabl e. Neurons are faced with a similar 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 th an as a scalar. We show how a nonlinear recurrent network can be used to perform estimation in a near-optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.