A unified approach to linking experimental, statistical and computational analysis of spike train data.
A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach--linking statistical, computational, and experimental neuroscience--provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America.
Liang
L
Meng
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America.
Mark A
MA
Kramer
Hull York Medical School, York University, York, United Kingdom.
Steven J
SJ
Middleton
Hull York Medical School, York University, York, United Kingdom.
Miles A
MA
Whittington
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America.
Uri T
UT
Eden
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D055592:Biophysical Phenomena; D003198:Computer Simulation; D008297:Male; D008959:Models, Neurological; D051381:Rats; D017208:Rats, Wistar; D013223:Statistics as Topic