Event-scheduling algorithms with Kalikow decomposition for simulating potentially infinite neuronal networks
This work addresses a computational bottleneck for realistic brain modeling by providing a tractable method to simulate infinite networks, though it is incremental as it adapts existing decomposition techniques to continuous time.
The authors tackled the problem of simulating infinite neuronal networks in continuous time by proposing an event-scheduling algorithm with Kalikow decomposition, enabling sequential simulation without approximations for point process models.
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.