On the relationship between LFP & spiking data

David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
Pages2060-2068
Number of pages9
StatePublished - Jan 1 2014
Externally publishedYes

Fingerprint Dive into the research topics of 'On the relationship between LFP & spiking data'. Together they form a unique fingerprint.

Cite this