Nonlinear model of single hippocampal neurons with dynamical thresholds.
Neurons transform a series of presynaptic spikes into a series of postsynaptic spikes through a number of nonlinear mechanisms. A nonlinear model with a dynamical threshold was built using a Volterra Laguerre kernel method to characterize the spike train to spike train transformations of hippocampal CA1 pyramidal neurons. Inputs of the model were broadband Poisson random impulse trains with a 2 Hz mean frequency, and outputs of the model were the corresponding evoked post-synaptic potential (PSP) and spike train data recorded from CA1 cell bodies using a whole-cell recording technique. The model consists of four major components, i.e., feedforward kernels representing the transformation of presynaptic spikes to PSPs; a dynamical threshold kernel determining threshold value based on output inter-spike-intervals (ISIs); a spike detector; and a feedback kernel representing the spike-triggered after-potentials.
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
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D056547:CA1 Region, Hippocampal / Q000473:pathology*; D004594:Electrophysiology / Q000379:methods; D006624:Hippocampus / Q000473:pathology*; D008297:Male; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D016012:Poisson Distribution; D051381:Rats; D017207:Rats, Sprague-Dawley; D012815:Signal Processing, Computer-Assisted; D013569:Synapses / Q000473:pathology; D009435:Synaptic Transmission