Marked point process variational autoencoder with applications to unsorted spiking activities.

ABSTRACT: Spike train modeling across large neural populations is a powerful tool for understanding how neurons code information in a coordinated manner. Recent studies have employed marked point processes in neural population modeling. The marked point process is a stochastic process that generates a sequence of events with marks. Spike train models based on such processes use the waveform features of spikes as marks and express the generative structure of the unsorted spikes without applying spike sorting. In such modeling, the goal is to estimate the joint mark intensity that describes how observed covariates or hidden states (e.g., animal behaviors, animal internal states, and experimental conditions) influence unsorted spikes. A major issue with this approach is that existing joint mark intensi

SUBMITTER: Shibue R 

PROVIDER: S-EPMC11684723 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature