Conference on Cognitive Computational Neuroscience

San Francisco • August 25 - 28, 2022

P-1.58

A Multivariate Point Process Model for Neural Spike Trains

Reza Ramezan, Meixi Chen, Martin Lysy, Paul Marriott, University of Waterloo, Canada
Session:
Posters 1 Poster
Track:
Cognitive science
Location:
Pacific Ballroom H-O
Presentation Time:
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -8)
Abstract:
The current state-of-the-art in neurophysiological data collection allows for simultaneous recording of tens to hundreds of neurons, for which point processes are an appropriate statistical modelling framework. However, existing point process models lack multivariate generalizations which are both flexible and computationally tractable. This paper introduces a multivariate generalization of the Skellam process with resetting (SPR), a point process tailored to model individual neural spike trains. The multivariate SPR (MSPR) is biologically justified as it mimics the process of neural integration. Its flexible dependence structure and a fast parameter estimation method make it well-suited for the analysis of simultaneously recorded spike trains from multiple neurons. The strengths and weaknesses of the MSPR are demonstrated through simulation and analysis of experimental data.
Manuscript:
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License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2022.1153-0
Publication:
2022 Conference on Cognitive Computational Neuroscience
Presentation
Discussion
Resources
No resources available.
Session P-1
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