Decoding population codes

Population coding is a coding scheme used in a neural systems and is of general importance. It is ubiquitous in neurological systems. For this reason there is great interest in exploiting population coding in pattern recognition algorithms. A population of neural activities represents not only the value of some variable in the environment, but a full probability distribution for that variable. The information is held in a distributed and encoded form which may in some situations be more robust to noise and failures than conventional representations. Encoding a population code with discrete-valued elements creates inaccuracies in the coded distributions. The result of these errors is the introduction of spurious high-frequency noise in the final distribution. We develop two methods of eliminating these errors and present results comparing the reconstruction accuracy of these techniques