Tracking population densities using dynamic neural fields with moderately strong inhibition
We discuss the ability of dynamic neural fields to track noisy population codes in an online fashion when signals are constantly applied to the recurrent network. To report on the quantitative performance of such networks we perform population decoding of the orientation embedded in the noisy signal and determine which inhibition strength in the network provides the best decoding performance. We also study the performance of decoding on time-varying signals. Simulations of the system show good performance even in the very noisy case and also show that noise is beneficial to decoding time-varying signals.
population decoding dynamic neural field model noise reduction