On the relationship between LFP & spiking data


Conference Paper

One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes. This work proposes a strategy for identifying neural networks characterized by time- and frequency-dependent connectivity patterns, using convolutional dictionary learning that links spike-train data to local field potentials (LFPs) across multiple areas of the brain. Analytical contributions are: (i) modeling dynamic relationships between LFPs and spikes; (ii) describing the relationships between spikes and LFPs, by analyzing the ability to predict LFP data from one region based on spiking information from across the brain; and (iii) development of a clustering methodology that allows inference of similarities in neurons from multiple regions. Results are based on data sets in which spike and LFP data are recorded simultaneously from up to 16 brain regions in a mouse.

Duke Authors

Cited Authors

  • Carlson, DE; Borg, JS; Dzirasa, K; Carin, L

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 3 / January

Start / End Page

  • 2060 - 2068

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus