RT Journal Article
SR Electronic
T1 Learning In Spike Trains: Estimating Within-Session Changes In Firing Rate Using Weighted Interpolation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 041301
DO 10.1101/041301
A1 Greg Jensen
A1 Fabian Muñoz
A1 Vincent P. Ferrera
YR 2016
UL http://biorxiv.org/content/early/2016/02/26/041301.abstract
AB The electrophysiological study of learning is hampered by modern procedures for estimating firing rates: Such procedures usually require large datasets, and also require that included trials be functionally identical. Unless a method can track the real-time dynamics of how firing rates evolve, learning can only be examined in the past tense. We propose a quantitative procedure, called ARRIS, that can uncover trial-by-trial firing dynamics. ARRIS provides reliable estimates of firing rates based on small samples using the reversible-jump Markov chain Monte Carlo algorithm. Using weighted interpolation, ARRIS can also provide estimates that evolve over time. As a result, both real-time estimates of changing activity, and of task-dependent tuning, can be obtained during the initial stages of learning.