Cellular Mechanisms Underlying Spike-Time Reliability and Stochastic Synchronization: Insights and Predictions from the Phase-Response Curve

Abstract

Neurons can respond with highly consistent spike patterns to repetitions of the same stimulus. Analogously, similar neurons receiving a common stimulus can fire highly consistent spike patterns. The former phenomenon is referred to as spike-time reliability, whereas the latter is an example of stochastic synchronization. Both phenomena are quite general and in fact, they also manifest in simplified models of single neuron dynamics, like phase oscillator models, in which the activity of the neuron is determined by its phase-response curve, which in turn is determined by the membrane conductances. Here, we use two measures of spike-time reliability and stochastic synchronization for real neurons and conductance-based models that have been recently introduced in the theory of phase oscillators: the Lyapunov exponent of the oscillator dynamics and the variance of the phase difference between two identical oscillators. Analyzing data from simulations and experiments, we show that, in response to manipulations of membrane conductances, a change of the phase-response curve leading to lower variance of the relative phase is a good predictor of increased spike-time reliability and stochastic synchronization in real and simulated neurons. We also explain why the Lyapunov exponent is not sufficient by itself. Our approach is then exemplified by investigating the effect of certain potassium currents, A and A-like currents, on spike-time precision. Finally, we discuss the biological relevance of our results.