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The in-vitro study of the rat intermediate SC by Lee and Hall did not find evidence for the long-range inhibitory/short-range excitatory connection pattern theorized by proponents of the neural-field theory of SC fixation.

Cuijpers and Erlhagen use neural fields to implement Bayes' rule for combining the activities of neural populations spatially encoding probability distributions.

Beck et al. argue that simply adding time point-to-time point responses of a population code will integrate the information optimally if the noise in the input is what they call "Poisson-like".

That is somewhat expected as in a Poisson distribution with mean $\lambda$ the variance is $\lambda$ and the standard deviation is $\sqrt{\lambda}$ and adding population responses is equivalent to counting spikes over a longer period of time, thus increasing the mean of the distribution.