Dynamic Analysis Of CA1&CA3Pyramidal Neurons Model

Pyramidal neurons are a type of neuron located in cerebral cortex, the tonsils andhippocampus, they play an important role in firing rhythm. The firing patterns of pyramidalneuron from regular spike change in the spontaneous bursting, bursting as a typical feature ofpyramidal neuron, plays an important role in the electrical generation, transmission andsynaptic plasticity. There are many factors affect the firing patterns of pyramidal neuron, suchas the channel type, the concentration and ion channel activity of ions, membrane potentialand external stimuli and so on. The ion channels of pyramidal neurons model in this paper aredescribed by the classical HH equations.This paper, we systematically analyzed two-compartment model of CA3pyramidalneuron and multi-compartment model of CA1pyramidal neuron, we studied the physiologicalparameters affect the firing patterns, such as the membrane capacitance, external stimulationcurrent, equilibrium potential and connection strength. And in the multi-compartment modelof CA1pyramidal neuron, we only consider ion current in the soma and ignore in thedendrites, but in the two-compartment model of CA3pyramidal neuron, ion current appear inboth the soma and dendrites. As we can see from the results of numerical simulations, withthe change of various physiological parameters, resulting in rich firing patterns, such asspiking and bursting, and the change of spikes in each bursting is regular. With the change ofphysiological parameters, The firing patterns of the model contains rich nonlinear dynamicalproperties, such as period doubling bifurcation, period adding bifurcation, InterSpike Intervals,and so on, it makes us have further understand between the relationship of the firing patternsof action potential and physiological parameters in pyramidal neuron.In this paper, the numerical simulation of two models were using4-th Runge-Kuttamethod by MATLAB software. The numerical results can help us have more understandingthe firing patterns and information encoding of pyramidal neuron.