Applications of Hawkes processes in biological neural networks.
Abstract
Neurons communicate by generating brief electrical impulses known as spikes. A widely used framework for representing spike trains in a neural network is the multivariate Hawkes process. In this talk, I will present two applications of Hawkes models in biological neural networks.
In the first part, I will introduce a biologically plausible spiking neural network model for decision-making tasks, in which neuronal activity is represented using Hawkes processes. We established strong approximation results between this network and drift–diffusion models (DDMs), commonly employed in cognitive science to account for choice behavior and reaction times, which enables to bridge the gap between cognitive and biological models.
In the second part, I will describe a new method for inferring neuronal interactions from partially observed spike train data. This method leverages first- and second-order moment statistics of the Hawkes process to reconstruct functional connectivity.