The Nonlinear Characteristic Of Excitability In Mesencephalic V Neurons

The most significant feature of neurons is their excitability. For a long time, the expressional manner, mechanism of ion currents, and its changing rule of excitability have been detected quite well, while little is known about the following questions: the reason that different responsiveness of neuron under the same stimulus; the reason that neuron could exhibit various firing patterns within the same ion currents; the effect of noise to excitability, et al. We investigate some basic problems about excitability through combining electrophysiological experiments with nonlinear theory. The aim is try to expose some main nonlinear characteristics of excitability, in order to well realize the essential process of excitability.First part: Classification and Transformation of Excitability in Mesencephalic V NeuronsExcitability is the ability that excitable cells are able to produce the action potential (AP) when stimulated. Traditionally, we estimated it just through the intensity of stimulus threshold and the number of AP. It is Hodgkin (1948) who first classified the excitability into three classes according to the relationship of firing frequency and applied current intensity, in a study of crustacean axon, but didn't bring enough attention. In 1980s, with the development of nonlinear theory and computation technology, theory scientist demonstrated the excitability classification and its dynamic mechanism by using some mathematical models. In real neurons, especially mammal's neuron, however, it is still not clear about the excitability classification and the relation among them, and the mechanism of them. Our research not only finds that there existed the excitability classification in mammal's neuron, but also testifies the transformation among them. Obviously, with the further investigation to excitability classification, we will transcend the limitation of traditional conception and could establish the basis to advance explore the intrinsic essence of excitability.In our study, the mesencephalic V (Mes V) neurons slices from neonatal rats were adopted as specimen. Whole-cell recording by infrared visual patch clamp was combined with pharmacological technique.Main results:1. 140 Mes V neurons in our experiments were divided into three classes when giving the constant-current depolarize stimulus. Class 1 excitability neurons (9/140): low injected current produce spike discharges with a latency and the firing frequency increased continuously with the injected current intensity increase. Class 2 excitability neurons (55/140): relatively high injected current produce high frequency spike train without latency. The firing frequency was less sensitive to the injected current intensity. Class 3 excitability neurons (76/140): only fired a single AP at the relatively high injected current. Very high stimulation intensity (over 1000 pA) could evoke 3 to 5 action potentials.2. Ramp depolarize stimulus: the firing frequency of Class 1 neurons increased lineally from low to high with the depolarizing membrane potential. And before the AP, there is no subthreshold membrane potential oscillation (SMPO). Class 2 excitability neurons exhibited SMPO and the firing frequency remained relatively constant even though the magnitude of the injected current continually increased. Class 3 neurons didn't show any spike under the ramp injected current.3. 50μM 4-AP (low concentration could selectivity block I4-AP) transformed originally Class 2 and Class 3 excitability neurons into Class 1 excitability behavior.4. 2μM riluzole (a kind of non- specific blocker of persistent sodium current (INaP)) abolished the spike discharges and transformed originally Class 2 into Class 3 excitability neurons. But did not made obvious change to the originally Class 3 excitability neurons.5. First, blocked I4-AP with 4-AP could transform originally Class 2 into Class 1, then after additional riluzole application (means blocking INaP instead of resuming I4-AP), the spike discharges of this neuron transformed from Class 1 back into Class 2. Riluzole transformed the originally Class 2 excitability neuron into Class 3 type and additional 4-AP (means blocking I4-AP instead of resuming INaP) could restore the spike discharges like Class 2. We named these changes of the excitability classification as transformation of excitability. 6. During the process of the transformation of excitability, the cell exhibited mixed characteristics of different classes. We termed it as"intermediate phenomenon".7. 20 mM tetraethylammonium (TEA) (blocker of K+ current), 10μM ZD 7288 (specific blocker of Ih), and 300μM Cd2+ (blocker of Ca2+ current) did not transform neuron excitability class.8. The difference of activation threshold, V1/2 and k value were statistically significant between the Class 2 and Class 3 neuron. The mean amplitude of I4-AP was significantly smaller in Class 2 than in Class 3 type neurons when depolarized to and above -56 mV. There was no significant difference of the above kinetic characteristics and mean amplitude of current between two class neurons.9. The mathematics model simulations of Mes V neuron replicate the classification and the transformation of excitability that observed in experiments, indicate the relationship between excitability classification and the dynamic bifurcation (the work of mathematics model was finished by Doc. Liu Yihui).Main conclusions:1. For the first time testify that there exist three excitability classes in Mes V neuron, and could use ramp function to check it conveniently.2. Detect the transformation of excitability. The dynamic change of the relative amplitude proportion of I4-AP and INaP is the crucial mechanism in deciding which class of excitability behavior a Mes V neuron exhibits. 3. The neuron could exhibits"intermediate phenomenon"during the process of the transformation of excitability.4. The classification and the transformation of excitability were testified by using neuronal mathematics model.Second part: Noise increase the excitability and the relationship with its resonant character in Mes V neuronRecently, one of the important developments of overlapping research in the non-linear science and neuroscience is that some degree of noise can play a constructive role in the detection of weak signals. Moreover, theoretical model research indicates that neuron could take in the effect of noise through stochastic resonance (SR) or autonomous SR (ASR). Actually, neuron lives in an environment full of noise, such as the stochastic open and close of the ion channels, and the stochastic release of the neurotransimitters. In brain, however, little is known about whether and how the neuron accepts the effect of noise.Part of neurons exist the character that could selective amplify the input frequency, i.e., character of frequency resonance, and SMPOs were regarded as the exhibition of amplificatory frequency resonance. Through observing and comparing effect of noise to the three classes'excitability neuron, this research indicates the relationship between the effect of noise and the character of resonance, and could provide a new clue for clarifying the effect of noise in nerve system.Besides the same material and methods as the first part, we also add two kinds of stimulus way - Gauss white noise and ZAP (some kind of sine wave with constant amplitude and linear-increased frequency)Main results:1. All of the Class 2 neuron showed a dual voltage dependence electrical resonance, that means there have two forms of resonance frequency: the high resonance frequency were 75.4±2.11 Hz when the membrane potential was depolarized to -50 mV; the low resonance frequency were 5.46±0.31 Hz when the membrane potential was hyperpolarized to -70 mV.2. 50μM 4-AP could abolish the high frequency resonant peak, but caused little or no change of the low frequency resonant peak. 10μM ZD7288 could abolish the low frequency resonant peak, but caused little or no change of the high frequency resonant peak.3. There is no voltage dependence of resonance behavior in a Class 1 neuron.4. Parts of Class 3 neuron (13/21) also have two forms of resonance frequency, which are similar with Class 2 both in resonance frequency and resonance currents.5. In Class 2 neuron, noise with a certain range of intensity could reduce the membrane potential level of SMPO, the degree of such reduction was increase with the increment of noise intensity.6. In Class 2 neuron, noise with a certain range of intensity could reduce the membrane potential level of the first AP. With the increase of noise intensity, not only the degree of such reduction, but also the number and rhythm of spike were increase. The connection (βvalue) between spike rhythm and noise intensity is best when the intensity of noise reach the best(range: 150-250,n = 10). Then with the noise continuously increased, though the spike number increased, the rhythm of spike disordered and the connection decreased.7. Riluzole could inhibit the SMPO of Class 2, and eliminate the excitability effect that noise made.8. Class 1 (n = 12) and Class 3 (n = 20) neurons do not show any obvious responses to noise.Main conclusions:1. In the level of depolarized and hyperpolarized membrane potential, Class 2 neuron have two forms of resonance frequency, and respectively mediated by I4-AP and Ih.2. Noise could increase the excitability of Class 2 neuron by means of reducing both the membrane potential levels of SMPO and spike threshold.3. The effect that noise increasing the excitability of Class 2 neuron depends on the existence of SMPO, but the ion channels mechanism of it still needs more investigate.Third part: Relationship between responsiveness and dynamic state on sciatic pacemakerFor some time, the responsiveness of neurons has been detected under a resting background condition, while little is known about the response rule when neurons are stimulated under an active background in which neurons may display many kinds of firing patterns. Our previous works and other relative researches indicate that the responsiveness of neuron may depend on the dynamic states of its firing pattern. Period-adding bifurcation was a nonlinear phenomenon in excitable cells. Our research tries to determine the possible change of its responsiveness (also called excitability) when the dynamic states of neuron are belong to different time physics during bifurcation.We guide the discharge from the pacemaker of CCI model (Chronic Compression of Sciatic model) in rat using the single fiber recording technology, then change the calcium concentration of the solution perfusing the pacemaker, and make it possible for artificially maintained different dynamic states within period-adding bifurcation. Finally, we give the square-wave electrical field stimulus.Main results:1. Successfully recorded the whole process of period-adding bifurcation form A-type fiber in CCI model, and could distinguish different time physics during the bifurcation, such as near or far from the bifurcation point.2. In the time physic far from the bifurcation point, with the increase of the intensity of excitatory stimulus, the firing rate increased in an approximately linear manner and no firing pattern transition was observed. While in the time physic near the bifurcation point, the firing rate increased markedly higher accompanied with the transition of firing pattern when the intensity of excitatory stimulus remained the same. The stimulus-response of the time physic near the bifurcation point shifted upward significantly compared to that of the time physic far from the bifurcation point.3. Inhibitory stimulus with the same intensity, however, decreased the firing rate slightly without the transition of firing pattern in the region near the bifurcation point. Main conclusions:These results suggest that the responsiveness in the time physic near the bifurcation point is more sensitive than that in the time physic far from the bifurcation point, which we named"critical sensitivity", and this has directional selectivity. This phenomenon further supports our hypothesis that the responsiveness of neuron may depend on the dynamic states of its firing pattern.