Nonparametric modeling of single neuron.
Nonlinear dynamic models were built with Volterra Lagurre kernel method to characterize the input-output properties of single hippocampal CA1 pyramidal neurons. Broadband Poisson random impulse trains with a 2 Hz mean frequency, which include the majorities of the spike patterns in behaving rats, were used to stimulate the Schaffer collaterals. Corresponding random-interval post-synaptic potential (PSP) and spike train data were recorded from the cell bodies using whole-cell recording technique and then analyzed with the nonlinear dynamic model. The model consists of two major components, i.e., a feedforward three order Volterra kernel model characterizing the transformation of presynaptic stimulations to pre-threshold PSPs, and a feedback one order Volterra kernel model capturing the spike-triggered after-potential. Results showed that the model could predict 1) the sub-threshold PSPs trace with a normalized mean square error around 10% and 2) the spikes with accuracy higher than 80%.
Lu
Ude
U
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA. ulu@ usc.edu
Song
Dong
D
Berger
Theodore W
TW
D000465:Algorithms; D000818:Animals; D004566:Electrodes; D004594:Electrophysiology / Q000379:methods; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000378:metabolism / Q000502:physiology*; D017711:Nonlinear Dynamics; D016012:Poisson Distribution; D017966:Pyramidal Cells / Q000473:pathology*; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D055366:Synaptic Potentials