Effect of lateral connections on the accuracy of the population code for a network of spiking neurons
We study how neuronal connections in a population of spiking neurons affect the accuracy of stimulus estimation. Neurons in our model code for a one-dimensional orientation variable $\phi$. Connectivity between two neurons depends on the absolute difference $|\phi-\phi'|$ between the preferred orientation of the two neurons. We derive an analytical expression of the activity profile for a population of neurons described by the spike response model with noisy threshold. We estimate the stimulus orientation and the trial-to-trial fluctuations using the population vector method. For stationary stimuli, uniform inhibitory connections produce a more reliable estimation of the stimulus than short-range excitatory connections with long-range inhibitions, although the latter interaction type produces a sharper tuning curve. These results are consistent with previous analytical studies of the Fisher information.