Fisher information and temporal correlations for spiking neurons with stochastic dynamics

Population coding accuracy can be studied using Fisher information. Here the Fisher information and correlation functions are determined analytically for a network of coupled spiking neurons with a more general than Poisson stochastic dynamics. It is shown that stimulus-driven temporal correlations between neurons always increase the Fisher information, whereas stimulus-independent correlations need not do so. Additionally, we find that for subthreshold stimuli there is some nonzero level of noise for which network coding is optimal. We also find that the Fisher information is larger for purely excitatory than for purely inhibitory networks, but only in a limited range of values of synaptic coupling strengths. In most cases the dependence of the Fisher information on time is linear, except for excitatory networks with strong synaptic couplings and for strong stimuli. In the latter case this dependence shows two distinct regimes: fast and slow. For excitatory networks short-term synaptic depression can improve the coding accuracy significantly, whereas short-term facilitation can lower the coding accuracy. For inhibitory networks, coding accuracy is insensitive to short-term synaptic dynamics.