When neurons are excited, they "fire": a rapid voltage change that is detectable and visible as a "spike" in electrophysiology recordings. Rapid bursts of spikes - or, inversely, long pauses with no spikes - are of interest to neuroscientists testing various drug conditions and their neural effects in the lab. However, while many algorithms have been proposed to identify bursts and pauses from spikes in recorded electrophysiology data, these methods are often highly specific to particular cell lines and systems and are not widely tested across them. In collaboration with MSI, the lab of Julia Lemos (assistant professor, Neuroscience) is assessing the performance and fit of various algorithms, including machine learning detection techniques, that could identify neural bursting and pause events for various cell types.

MSI staff members Dr. Jeff Shi and Dr. Ying Zhang have developed learning applications that have been implemented into a user-friendly R interface (ShinyApp), which the Lemos lab is using.

Research Computing partners:

• Minnesota Supercomputing Institute