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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#연습문제 3\n", | |
| "\n", | |
| "###본 연습문제는 1주차 Neural Simulation 복습 및 function와 class 를 정의해보고 이를 활용해 보는 것을 목표으로 합니다.\n", | |
| "\n", | |
| "Neural simulation 중에서 가장 널리 활용되는 Hodkins-Huxly(HH) 모델을 이용해 실습을 수행할 것입니다. function 생성에 집중하기 위해 1주차때 수행되었던 소스코드는 간단한 해설과 같이 제공됩니다. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Populating the interactive namespace from numpy and matplotlib\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING: pylab import has clobbered these variables: ['pylab']\n", | |
| "`%matplotlib` prevents importing * from pylab and numpy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%pylab inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "위 명령어는 시각화모듈인 matplotlib에서 제공하는 함수를 빌트인 함수처럼 호출이 가능하도록 해주며 출력되는 그래프를 output 란에 출력이 가능하도록 해주는 명령어입니다." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from numpy import * # numpy 모듈을 불러오기\n", | |
| "from __future__ import division, print_function, absolute_import, unicode_literals" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# import numpy\n", | |
| "# print(numpy.__version__)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "- numpy는 numerical python 이라는 모듈로 python 내에서 다양한 수학적 연산처리를 수행하는데 편리한 함수들을 제공하는 모듈입니다.\n", | |
| "- 일반적으로 numpy 모듈을 import 하는 경우 다음과 같은 2가지 방법 중에 1가지를 사용합니다.\n", | |
| "\n", | |
| ">import numpy as np\n", | |
| "\n", | |
| ">from numpy import *\n", | |
| "\n", | |
| "- 위의 방법은 모듈을 np라는 약어로 호출해오는 경우로 numpy 모듈 내 함수를 불러오기 위해선 np.[function name]의 형태를 사용합니다\n", | |
| "- 아래의 방법은 numpy 모듈의 모든함수(*)를 직접 불러오기 때문에 빌트인 함수처럼 사용할 수 있는 장점이 있지만, 너무 많은 모듈을 위와 같은 형태로 불러들이면 함수명이 중첩되는 경우 처리가 곤란해집니다." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### 아래는 1주차 수업때 수행했던 신경세포의 HH 모델을 시뮬레이션 하기 위한 파이썬 코드입니다." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "$$\\frac{dV}{dt} = \\frac{1}{C_m}(I_m - g_{Na}m^3h(V-E_{Na})-g_Kn^4(V-E_K)-g_l(V-E_l))$$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# K channel\n", | |
| "alpha_n = vectorize(lambda v: 0.01*(-v + 10)/(exp((-v + 10)/10) - 1) if v != 10 else 0.1)\n", | |
| "beta_n = lambda v: 0.125*exp(-v/80)\n", | |
| "n_inf = lambda v: alpha_n(v)/(alpha_n(v) + beta_n(v))\n", | |
| "\n", | |
| "# Na channel (activating)\n", | |
| "alpha_m = vectorize(lambda v: 0.1*(-v + 25)/(exp((-v + 25)/10) - 1) if v != 25 else 1)\n", | |
| "beta_m = lambda v: 4*exp(-v/18)\n", | |
| "m_inf = lambda v: alpha_m(v)/(alpha_m(v) + beta_m(v))\n", | |
| "\n", | |
| "# Na channel (inactivating)\n", | |
| "alpha_h = lambda v: 0.07*exp(-v/20)\n", | |
| "beta_h = lambda v: 1/(exp((-v + 30)/10) + 1)\n", | |
| "h_inf = lambda v: alpha_h(v)/(alpha_h(v) + beta_h(v))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.text.Text at 0x10d5f6390>" | |
| ] | |
| }, | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
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| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10d7e7410>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "### Channel Activity ###\n", | |
| "v = arange(-50,151) # mV\n", | |
| "figure()\n", | |
| "plot(v, m_inf(v), v, h_inf(v), v, n_inf(v))\n", | |
| "legend(('Na','Na (Inactivating)','K'))\n", | |
| "title('Steady state values of ion channel gating variables')\n", | |
| "ylabel('Magnitude')\n", | |
| "xlabel('Voltage (mV)')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "## setup parameters and state variables\n", | |
| "T = 55 # ms\n", | |
| "dt = 0.025 # ms\n", | |
| "time = arange(0,T+dt,dt)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "## HH Parameters\n", | |
| "V_rest = 0 # mV\n", | |
| "Cm = 1 # uF/cm2\n", | |
| "gbar_Na = 120 # mS/cm2\n", | |
| "gbar_K = 36 # mS/cm2\n", | |
| "gbar_l = 0.3 # mS/cm2\n", | |
| "E_Na = 115 # mV\n", | |
| "E_K = -12 # mV\n", | |
| "E_l = 10.613 # mV\n", | |
| "\n", | |
| "Vm = zeros(len(time)) # mV\n", | |
| "Vm[0] = V_rest\n", | |
| "m = m_inf(V_rest) \n", | |
| "h = h_inf(V_rest)\n", | |
| "n = n_inf(V_rest)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "## Stimulus\n", | |
| "I = zeros(len(time))\n", | |
| "for i, t in enumerate(time):\n", | |
| " if 5 <= t <= 30: I[i] = 10 # uA/cm2 (stimulus_onset <= t <= stimulus_end 를 의미하는 부분)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([ 0., 0., 0., ..., 0., 0., 0.])" | |
| ] | |
| }, | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "Vm" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "## Simulate Model\n", | |
| "for i in range(1,len(time)):\n", | |
| " g_Na = gbar_Na*(m**3)*h\n", | |
| " g_K = gbar_K*(n**4)\n", | |
| " g_l = gbar_l\n", | |
| "\n", | |
| " m += (alpha_m(Vm[i-1])*(1 - m) - beta_m(Vm[i-1])*m) * dt\n", | |
| " h += (alpha_h(Vm[i-1])*(1 - h) - beta_h(Vm[i-1])*h) * dt\n", | |
| " n += (alpha_n(Vm[i-1])*(1 - n) - beta_n(Vm[i-1])*n) * dt\n", | |
| "\n", | |
| " Vm[i] = Vm[i-1] + (I[i-1] - g_Na*(Vm[i-1] - E_Na) - g_K*(Vm[i-1] - E_K) - g_l*(Vm[i-1] - E_l)) / Cm * dt\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([ 0.00000000e+00, 2.50000000e-02, 5.00000000e-02, ...,\n", | |
| " 5.49500000e+01, 5.49750000e+01, 5.50000000e+01])" | |
| ] | |
| }, | |
| "execution_count": 32, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "time" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([-30., -30., -30., ..., -30., -30., -30.])" | |
| ] | |
| }, | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "-30+I" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.text.Text at 0x10d7e7a50>" | |
| ] | |
| }, | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
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| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10d852950>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "## plot membrane potential trace\n", | |
| "figure()\n", | |
| "plot(time, Vm, time, -30+I)\n", | |
| "legend(('action potential', 'input pulse'))\n", | |
| "title('Hodgkin-Huxley Example')\n", | |
| "ylabel('Membrane Potential (mV)')\n", | |
| "xlabel('Time (msec)')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "source": [ | |
| "## 연습문제 3-1. 위의 코드를 이용해 다음 함수를 작성하시오.\n", | |
| "- T, dt, stimulus_onset, stimulus_end 를 input 으로 받아 [time, Vm, I] 을 리턴해주는 hh_simulate 함수\n", | |
| "- time, Vm, I 를 input 으로 받아 상기 Hodgkin-Huxley Example 그래프를 출력해주는 hh_plot 함수" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Populating the interactive namespace from numpy and matplotlib\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING: pylab import has clobbered these variables: ['pylab']\n", | |
| "`%matplotlib` prevents importing * from pylab and numpy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%pylab inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from __future__ import division, print_function, absolute_import, unicode_literals\n", | |
| "import numpy as np \n", | |
| "import pylab\n", | |
| "\n", | |
| "# K channel\n", | |
| "alpha_n = np.vectorize(lambda v: 0.01 * (-v + 10) / (np.exp((-v + 10) / 10) - 1) if v != 10 else 0.1)\n", | |
| "beta_n = lambda v: 0.125 * np.exp(-v / 80)\n", | |
| "n_inf = lambda v: alpha_n(v) / (alpha_n(v) + beta_n(v))\n", | |
| "\n", | |
| "# Na channel (activating)\n", | |
| "alpha_m = np.vectorize(lambda v: 0.1 * (-v + 25) / (np.exp((-v + 25) / 10) - 1) if v != 25 else 1)\n", | |
| "beta_m = lambda v: 4 * np.exp(-v / 18)\n", | |
| "m_inf = lambda v: alpha_m(v) / (alpha_m(v) + beta_m(v))\n", | |
| "\n", | |
| "# Na channel (inactivating)\n", | |
| "alpha_h = lambda v: 0.07 * np.exp(-v / 20)\n", | |
| "beta_h = lambda v: 1 / (np.exp((-v + 30) / 10) + 1)\n", | |
| "h_inf = lambda v: alpha_h(v) / (alpha_h(v) + beta_h(v))\n", | |
| "\n", | |
| "\n", | |
| "def hh_simulate(T, dt):\n", | |
| " time = np.arange(0, T + dt, dt)\n", | |
| "\n", | |
| " # HH Parameters\n", | |
| " V_rest = 0 # mV\n", | |
| " Vm = np.zeros(len(time)) # mV\n", | |
| " Vm[0] = V_rest\n", | |
| "\n", | |
| " ## Stimulus\n", | |
| " I = np.zeros(len(time))\n", | |
| " for i, t in enumerate(time):\n", | |
| " if 5 <= t <= 30:\n", | |
| " I[i] = 10 # uA/cm2 (stimulus_onset <= t <= stimulus_end 를 의미하는 부분)\n", | |
| " return time, Vm, I\n", | |
| "\n", | |
| "\n", | |
| "def hh_plot(time, Vm, I):\n", | |
| " Cm = 1 # uF/cm2\n", | |
| " gbar_Na = 120 # mS/cm2\n", | |
| " gbar_K = 36 # mS/cm2\n", | |
| " gbar_l = 0.3 # mS/cm2\n", | |
| " E_Na = 115 # mV\n", | |
| " E_K = -12 # mV\n", | |
| " E_l = 10.613 # mV\n", | |
| "\n", | |
| " V_rest = 0 # mV\n", | |
| " m = m_inf(V_rest)\n", | |
| " h = h_inf(V_rest)\n", | |
| " n = n_inf(V_rest)\n", | |
| "\n", | |
| " # Simulate Model\n", | |
| " for i in range(1, len(time)):\n", | |
| " g_Na = gbar_Na * (m ** 3) * h\n", | |
| " g_K = gbar_K * (n ** 4)\n", | |
| " g_l = gbar_l\n", | |
| "\n", | |
| " m += (alpha_m(Vm[i - 1]) * (1 - m) - beta_m(Vm[i - 1]) * m) * dt\n", | |
| " h += (alpha_h(Vm[i - 1]) * (1 - h) - beta_h(Vm[i - 1]) * h) * dt\n", | |
| " n += (alpha_n(Vm[i - 1]) * (1 - n) - beta_n(Vm[i - 1]) * n) * dt\n", | |
| "\n", | |
| " Vm[i] = Vm[i - 1] + (I[i - 1] - g_Na * (Vm[i - 1] - E_Na) - g_K * (Vm[i - 1] - E_K) - g_l * (Vm[i - 1] - E_l)) / Cm * dt\n", | |
| " pylab.figure()\n", | |
| " pylab.plot(time, Vm, time, -30 + I)\n", | |
| " pylab.legend(('action potential', 'input pulse'))\n", | |
| " pylab.title('Hodgkin-Huxley Example')\n", | |
| " pylab.ylabel('Membrane Potential (mV)')\n", | |
| " pylab.xlabel('Time (msec)')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 41, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10d7989d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "T = 55 # ms\n", | |
| "dt = 0.025 # ms\n", | |
| "\n", | |
| "time, Vm, I = hh_simulate(T, dt)\n", | |
| "hh_plot(time, Vm, I)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 연습문제 3-2. 위의 코드를 이용해 다음 조건을 만족하는 Class 를 작성하시오.\n", | |
| "\n", | |
| "class HH_method:\n", | |
| "\n", | |
| " def __init__(self):\n", | |
| " pass\n", | |
| " \n", | |
| " def setup(self, T, dt):\n", | |
| " pass\n", | |
| " \n", | |
| " def stimulus(self, onset, end):\n", | |
| " pass\n", | |
| " \n", | |
| " def plot(self):\n", | |
| " pass\n", | |
| "\n", | |
| "- 위의 구조를 이용해 다음 명령어를 수행하였을때 상기 그래프와 동일한 그래프가 출력되는 클래스를 작성하시오\n", | |
| "\n", | |
| ">model = HH_method() # model 이라는 object 생성\n", | |
| "\n", | |
| ">model.setup(55, 0.025)\n", | |
| "\n", | |
| ">model.stumulus(5, 30)\n", | |
| "\n", | |
| ">model.plot()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from __future__ import division, print_function, absolute_import, unicode_literals\n", | |
| "import numpy as np\n", | |
| "import pylab\n", | |
| "\n", | |
| "\n", | |
| "class HH_method(object):\n", | |
| " def __init__(self):\n", | |
| " self.alpha_n = np.vectorize(lambda v: 0.01 * (-v + 10) / (np.exp((-v + 10) / 10) - 1) if v != 10 else 0.1)\n", | |
| " self.beta_n = lambda v: 0.125 * np.exp(-v / 80)\n", | |
| " self.n_inf = lambda v: self.alpha_n(v) / (self.alpha_n(v) + self.beta_n(v))\n", | |
| "\n", | |
| " # Na channel (activating)\n", | |
| " self.alpha_m = np.vectorize(lambda v: 0.1 * (-v + 25) / (np.exp((-v + 25) / 10) - 1) if v != 25 else 1)\n", | |
| " self.beta_m = lambda v: 4 * np.exp(-v / 18)\n", | |
| " self.m_inf = lambda v: self.alpha_m(v) / (self.alpha_m(v) + self.beta_m(v))\n", | |
| "\n", | |
| " # Na channel (inactivating)\n", | |
| " self.alpha_h = lambda v: 0.07 * np.exp(-v / 20)\n", | |
| " self.beta_h = lambda v: 1 / (np.exp((-v + 30) / 10) + 1)\n", | |
| " self.h_inf = lambda v: self.alpha_h(v) / (self.alpha_h(v) + self.beta_h(v))\n", | |
| "\n", | |
| " def setup(self, T, dt):\n", | |
| " self.dt = dt\n", | |
| " self.time = np.arange(0, T + dt, dt)\n", | |
| "\n", | |
| " # HH Parameters\n", | |
| " V_rest = 0 # mV\n", | |
| " self.Vm = np.zeros(len(self.time)) # mV\n", | |
| " self.Vm[0] = V_rest\n", | |
| "\n", | |
| "\n", | |
| " def stumulus(self, onset, end):\n", | |
| " # Stimulus\n", | |
| " self.I = np.zeros(len(self.time))\n", | |
| " for i, t in enumerate(self.time):\n", | |
| " if onset <= t <= end:\n", | |
| " self.I[i] = 10 # uA/cm2 (stimulus_onset <= t <= stimulus_end 를 의미하는 부분)\n", | |
| "\n", | |
| " Cm = 1 # uF/cm2\n", | |
| " gbar_Na = 120 # mS/cm2\n", | |
| " gbar_K = 36 # mS/cm2\n", | |
| " gbar_l = 0.3 # mS/cm2\n", | |
| " E_Na = 115 # mV\n", | |
| " E_K = -12 # mV\n", | |
| " E_l = 10.613 # mV\n", | |
| "\n", | |
| " V_rest = 0 # mV\n", | |
| " m = self.m_inf(V_rest)\n", | |
| " h = self.h_inf(V_rest)\n", | |
| " n = self.n_inf(V_rest)\n", | |
| "\n", | |
| " # Simulate Model\n", | |
| " for i in range(1, len(self.time)):\n", | |
| " g_Na = gbar_Na * (m ** 3) * h\n", | |
| " g_K = gbar_K * (n ** 4)\n", | |
| " g_l = gbar_l\n", | |
| "\n", | |
| " m += (self.alpha_m(self.Vm[i - 1]) * (1 - m) - self.beta_m(self.Vm[i - 1]) * m) * self.dt\n", | |
| " h += (self.alpha_h(self.Vm[i - 1]) * (1 - h) - self.beta_h(self.Vm[i - 1]) * h) * self.dt\n", | |
| " n += (self.alpha_n(self.Vm[i - 1]) * (1 - n) - self.beta_n(self.Vm[i - 1]) * n) * self.dt\n", | |
| "\n", | |
| " self.Vm[i] = self.Vm[i - 1] + (self.I[i - 1] - g_Na * (self.Vm[i - 1] - E_Na) - g_K * (self.Vm[i - 1] - E_K) - g_l * (\n", | |
| " self.Vm[i - 1] - E_l)) / Cm * self.dt\n", | |
| "\n", | |
| " def plot(self):\n", | |
| " pylab.figure()\n", | |
| " pylab.plot(self.time, self.Vm, self.time, -30 + self.I)\n", | |
| " pylab.legend(('action potential', 'input pulse'))\n", | |
| " pylab.title('Hodgkin-Huxley Example')\n", | |
| " pylab.ylabel('Membrane Potential (mV)')\n", | |
| " pylab.xlabel('self.time (msec)')\n", | |
| " pylab.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10ccdb8d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "if __name__ == '__main__':\n", | |
| " model = HH_method() # model 이라는 object 생성\n", | |
| " model.setup(55, 0.025)\n", | |
| " model.stumulus(5, 30)\n", | |
| " model.plot()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.9" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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