Statistical analysis of neuronal population codes for encoding acute pain

Zhe Chen; Jing Wang

ICASSP 2016accepted0 citations

Statistical analysis of neuronal population codes for encoding acute pain

Abstract

To date most pain studies have focused on spinal cord or peripheral pathways. However, a complete understanding of pain mechanisms requires the study of neocortex. Using an animal model of acute pain, we investigate neural codes for pain at both single-cell and population levels. We propose a statistical framework, rooted in state space analysis, for analyzing neural ensembles recorded from the rat primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) during a laser pain stimulation protocol. The state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the "neuronal threshold" for pain on a single or multiple-trial basis.

BibTeX

@inproceedings{icassp2016_statisticalanaly,
  title = {Statistical analysis of neuronal population codes for encoding acute pain},
  author = {Zhe Chen and Jing Wang},
  booktitle = {ICASSP 2016},
  year = {2016}
}