Speaker
Description
Our lab uses multi-electrode probes to monitor the electrical activity from
populations of neurons in the gustatory cortex of the mouse as it drinks a liquid. We
simultaneously measure when licks occur. Is it possible to use the neural recordings
to determine when licks occur or what tastant is presented at various time points? We
describe a new method for doing this using functional networks and analysis tools from
network science. We demonstrate that this tool, NeuroSeq, is capable of determining
dynamic state changes in the activity of the neural ensemble, and that many of these
correspond to stimuli or mouse behavior. This functional network approach
is an alternative to Hidden Markov Models that allows the user more control in
determining what features of the data are used in the determination of states and state
changes. It is generally applicable to the analysis of spike trains from any
brain region.
