Signal-To-Noise-Optimal scaling of heterogenous population codes

摘要

Similarity measures for neuronal population responses that are based on scalar products can be little informative if the neurons have different firing statistics. Based on signal-to-noise optimality, this paper derives positive weighting factors for the individual neurons' response rates in a heterogeneous neuronal population. The weights only depend on empirical statistics. If firing follows Poisson statistics, the weights can be interpreted as mutual information per spike. The scaling is shown to improve linear separability and clustering as compared to unscaled inputs.