Event-scheduling algorithms with Kalikow decomposition for simulating potentially infinite neuronal networks

TL;DR

This paper introduces a novel event-scheduling algorithm with Kalikow decomposition for simulating potentially infinite neuronal networks in continuous time, enabling more realistic brain modeling.

Contribution

It develops a new continuous-time event-scheduling algorithm based on Kalikow decomposition, allowing efficient simulation of infinite neuronal networks.

Findings

Algorithm successfully simulates infinite networks in continuous time.

Enables realistic brain modeling with finite computational resources.

Improves upon Ogata's thinning strategy for large-scale networks.

Abstract

Event-scheduling algorithms can compute in continuous time the next occurrence of points (as events) of a counting process based on their current conditional intensity. In particular event-scheduling algorithms can be adapted to perform the simulation of finite neuronal networks activity. These algorithms are based on Ogata's thinning strategy \cite{Oga81}, which always needs to simulate the whole network to access the behaviour of one particular neuron of the network. On the other hand, for discrete time models, theoretical algorithms based on Kalikow decomposition can pick at random influencing neurons and perform a perfect simulation (meaning without approximations) of the behaviour of one given neuron embedded in an infinite network, at every time step. These algorithms are currently not computationally tractable in continuous time. To solve this problem, an event-scheduling algorithm with Kalikow decomposition is proposed here for the sequential simulation of point processes neuronal models satisfying this decomposition. This new algorithm is applied to infinite neuronal networks whose finite time simulation is a prerequisite to realistic brain modeling.