Analytical investigation of the effects of lateral connections on the accuracy of population coding.

We studied how lateral connections affect the accuracy of a population code by using a model of orientation selectivity in the primary visual cortex. Investigating the effects of lateral connections on population coding is a complex problem because these connections simultaneously change the shape of tuning curves and correlations between neurons. Both of these changes caused by lateral connections have to be taken into consideration to correctly evaluate their effects. We propose a theoretical framework for analytically computing the Fisher information, which measures the accuracy of a population code, in stochastic spiking neuron models with refractory periods. Within our framework, we accurately evaluated both the changes in tuning curves and correlations caused by lateral connections and their effects on the Fisher information. We found that their effects conflicted with each other and the answer to whether or not the lateral connections increased the Fisher information strongly depended on the intrinsic properties of the model neuron. By systematically changing the coupling strengths of excitations and inhibitions, we found the parameter regions of lateral connectivities where sharpening of tuning curves through Mexican-hat connectivities led to an increase in information, which is in contrast to some previous findings.