Fisher information and temporal correlations for spiking neurons with stochastic dynamics

Population coding accuracy can be studied using Fisher information. Here th e Fisher information and correlation functions are determined analytically for a network of coupled spiking neurons with a more general than Poisson s tochastic dynamics. It is shown that stimulus-driven temporal correlations between neurons always increase the Fisher information, whereas stimulus-in dependent correlations need not do so. Additionally, we find that for subth reshold stimuli there is some nonzero level of noise for which network codi ng is optimal. We also find that the Fisher information is larger for purel y excitatory than for purely inhibitory networks, but only in a limited ran ge of values of synaptic coupling strengths. In most cases the dependence o f the Fisher information on time is linear, except for excitatory networks with strong synaptic couplings and for strong stimuli. In the latter case t his dependence shows two distinct regimes: fast and slow. For excitatory ne tworks short-term synaptic depression can improve the coding accuracy signi ficantly, whereas short-term facilitation can lower the coding accuracy. Fo r inhibitory networks, coding accuracy is insensitive to short-term synapti c dynamics.