Nerve I, Type Ii Excited Kinetic Mechanism

Neurons are able to generate action potentials under stimulation and response in different manner under the actions of varied external stimulations. The different abilities and characteristics of a neuron represent its excitability. Traditional definition of neuronal excitability is based on the neuron's ability to produce action potentials under certain stimulation. Excitability is the intrinsic property of the neuron. It can be defined by the threshold potential of the neuron or by threshold stimulation applied externally. The electrophysiological property of the neuron can thus be represented by such threshold indexes. Recent investigations have revealed that neuronal excitability can also be defined by various firing patterns to the external stimulation. Therefore, firing patterns of a neuron can also reflect its intrinsic property.With combination of neuroscience, nonlinear dynamics, information technology, and computer technology, a new scientific approach which combines theory and experiment, is undergoing its formation. It is named as Neurodynamics and concerns preliminary the studies on complex neural firing patterns, their underlying mechanisms, as well as their transition regularities. This approach is providing new progress in the theoretical studies of neuronal firing rhythms and information encoding.Adjustment of related parameters can drive the neuron to evolve from resting to firing. In this process, two different responses in neuronal firing patterns have been discovered in previous theoretical analysis, mathematical simulation, and experiments. The underlying excitabilities are named as class I excitability and class II excitability, respectively. Main characteristics of these two classes of excitabilities are as follows: class I excitable neuron can fire spikes with an arbitrary low frequency, it has a well-defined threshold manifold, it can distinguish between excitatory and inhibitory input, and it acts as an integrator; i.e. the higher the frequency of incoming spikes, the sooner it fires. Class II excitable neuron does not have a well-defined threshold manifold, it can fire in response to an inhibitory pulse, and it acts as a resonator; i.e. it responds preferentially to a certain (resonant) frequency of the input. In dynamics, class I excitability mainly corresponds to saddle-node-like bifurcation and class II excitability mainly corresponds to Hopf bifurcation. Recently, more and more attentions are paid to neural excitabilities and there dynamics to seek a deeper understanding of the basic characteristics of neurons.In the present study, using methods of nonlinear dynamics as well as neurophysiological experiments, we studied the responses of the two different neural excitabilities with stimulation by ramp current and noise, in spontaneous firing activities, and in real biological experiments. The experimental neural pacemaker was employed as the experimental model. According to the features of various firing rhythms, different mathematical models were specifically selected to simulate experimental phenomena.The main results are as follows:1. Numeric simulation results suggest: after the external current was changed to ramp current, frequency-current (f-I) curve would change when the slope of ramp current was changed. With the increase of the slope of the ramp current, f-I curve was shifted to top right, suggesting the increase of initial firing frequency.In class I excitability parameter set, when the slope of ramp current was larger than a certain value, the initial firing frequency would increase to the value similar to that of the class II excitability. This leads to difficulties in discrimination of class I excitability and class II excitability.2. Stochastic simulation results show that: noise could shift f-I curve to left and decrease the minimum current which can induce firing. Because noise decreases the threshold of class II excitability, the minimal frequency presented in class II parameter set with noise would approach to zero and then, the f-I curve would look like that of class I excitability. So it would be difficult to distinguish between class I excitability and class II excitability.3. Under conditions with appropriately selected parameter values and noise intensities, different excitable system would present different firing rhythms, and different bifurcations could lead to different firing rhythms in the same excitable systems as well. The firing pattern in class I excitability parameter set was complex and seemed to be stochastic. In class II excitability parameter set, it would exhibit intermittent rhythm when the system underwent Subcritical Hopf bifurcation, and it would exhibit multi integer rhythm when the system undergoes Supercritical Hopf bifurcation.4. Stimulated by high frequent pulse, class I excitability presented quasiperiod rhythm; class II excitability presented chaotic multi-mode rhythm, but this was different to multiple integer.5. In experiments, gradual decrease of extracellular calcium concentration ([Ca²⁺]_o) induced transitions from resting to period 1 firing via intermittent periodic firing or multiple integer firing. These results show that the neurons are class II excitabilities although the firing frequency increases from zero, which was very similar to the character of class I excitability.When perfused with 5mmol/L [Ca²⁺]_o, the neural firing pattern transited to rest via intermittent period rhythm and multiple integer rhythm. Then, perfused with 0.1 mmol/L 4-aminopyfidine (4-AP) and 1.2 mmol/L [Ca²⁺]_o, the neuron transited from rest state to intermittent period firing too. And then, maintaining the concentration of 4-AP 0.1 mmol/L and increasing [Ca²⁺]_o to 5 mmol/L, the neuron still exhibited intermittent period firing but not rest. The present research proposes that: in class I excitability parameter set, when the slope of ramp current was larger than a certain value, the initial firing frequency would increase to the value similar to that of class II excitability, and these phenomenon suggested the slope of ramp current is important to estimate neural excitability; on the condition of noise, f-I curve of class II excitability looked like that of class I excitability, and this suggested it isn't proper that neural excitability was estimated only according to f-I curve; the statistical characters are different between two classes of excitabilities; moreover, experimental results proved spontaneous firing in mathematical simulation and frequency-changing curve under noise, so the firing rhythm could be criterion to estimate neural excitability but not frequency-changing curve. All of the above make us understand comprehensively about the neural excitability.