layout | title | date | author | summary | weight |
---|---|---|---|---|---|
notes |
07.Two dimensional neuron models |
2016-04-21 |
harryhare |
Two dimensional neuron models |
7 |
- Origin model:
$\Sigma I_{k}=g_{Na}m^{3}h(u-E_{Na})+g_{K}n^{4}(u-E_{k})+g_{L}(u-E_{L})$
It has 4arguments
- In this chapter,we reduce it to 2 arguments:
$u$ ,$\omega$ $C\frac{du}{dt}=-g_{Na}[m_0(u)]^{3}(b-\omega)(u-E_{Na})-g_{k}(\frac{\omega}{a})^{4}(u-E_{k})-g_{L}(u-E_{L})+I$
- What changes?
because of
m
changes fast:$m(u,t)$ ->$m_{0}(u)$ because ofh
andn
seems to have linear relationship:$h$ ->$b-\omega$ $n$ ->$\frac{\omega}{a}$
It seems that this model just change exponents of arguments to get a linear equation.
This section also give a approximate equation for $m_{0}(u)$and
On phase plane, point
-
$u$ -nullcline: -points with$\dot{u} = 0$ . -The direction of flow on the u-nullcline is in direction of -$(0,\dot{\omega})$ -vertical -
$\omega$ -nullcline: -points with$\dot{\omega} = 0$ -$(0,\dot{u})$ -horizontal - fix-point:
-intersection of
$u$ -nullcline and$\omega$ -nullcline -on nullclines the direction of arrows change at fix points
-
At fix point
$$\frac{d}{dt}\vec{x}=\left(\begin{array}{cc} F_{u} & F_{\omega} \ G_{u} & G_{\omega} \end{array} \right)\vec{x}$$
-
Set
$x(t) =\vec{e}e^{λt}$ ,we get eigenfunction: $$\lambda\vec{x}=\left(\begin{array}{cc}F_{u}&F_{\omega}\ G_{u}&G_{\omega}\end{array}\right)\vec{x}$$$\lambda_{1}+\lambda_{2} = F_{u}+G_{\omega}$ $\lambda_{1}\lambda_{2} = F_{u}G_{\omega}-F_{\omega}G_{u}$ -
Saddle point:
$\lambda_{1}>0$ $\lambda_{2}<0$ -
Stable points:
$\lambda_{1}<0$ $\lambda_{2}<0$ -
Unstable points:
$\lambda_{1}>0$ $\lambda_{2}>0$