In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that, despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models offer a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties to simulate their networks’ dynamics in silico with standard numerical discretization schemes. Indeed, evolving such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. To answer these issues, we designed an exact event-driven algorithm for the simulation of recurrent networks of perfect stochastic integrate-and- fire neurons with delayed Dirac-like interactions.
The two movies below illustrate the dynamics of two interacting neurons embedded in a larger network. The "synchronous picture" shows the instantaneous membrane voltage of each neuron fluctuating due to network interactions and internal noise. The "asynchronous picture" shows the progressive refinements of the next-spiking times of the two neurons.