🤖 AI Summary
This study addresses the challenge of modeling dynamic excitation effects in event-time data under repeated external stimuli. The authors propose a Hierarchical Excitation Process (HEP), which represents the conditional intensity as a superposition of time-evolving dynamic kernel functions and incorporates a hierarchical structure to yield interpretable modulation of stimulus responses. By integrating likelihood-based point process inference with model-driven clustering, the method simultaneously captures individual response dynamics and identifies latent subpopulations exhibiting similar excitation patterns. Experimental results demonstrate that HEP accurately recovers the underlying dynamic latent structure in synthetic data and effectively reveals time-varying neuronal excitability across different experimental conditions in spike train recordings from the abdominal ganglion of Aplysia.
📝 Abstract
We introduce the Hierarchical Excitatory Process (HEP), a flexible point process model for event-time data observed under repeated external stimuli. The proposed framework models the conditional intensity of a point process as a superposition of excitation effects induced by external stimuli, characterised by kernels with parameters dynamically evolving over time. This hierarchical construction enables modulation of excitation strength across repeated stimuli, providing an interpretable structure. We establish likelihood-based inference for the proposed model and embed HEP within a model-based clustering framework to identify latent groups sharing similar response dynamics. Simulation studies demonstrate the model's ability to recover evolving latent patterns, and an application to spike train recordings from the sea slug Aplysia pedal ganglion illustrates how HEPs are able to characterise stimulus-driven excitability of neurons across repeated stimulation under different experimental conditions.