[Pybrainsim-activity] SF.net SVN: pybrainsim:[132] trunk/src
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[Pybrainsim-activity] SF.net SVN: pybrainsim:[132] trunk/src
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From: <rg...@us...> - 2009-09-13 23:40:43
|
Revision: 132
http://pybrainsim.svn.sourceforge.net/pybrainsim/?rev=132&view=rev
Author: rgoj
Date: 2009-09-13 23:40:34 +0000 (Sun, 13 Sep 2009)
Log Message:
-----------
* PyBrainSimGUI.py becomes PyBrainSim.py
* We now have a graphical interface!
Added Paths:
-----------
trunk/src/PyBrainSim.py
Removed Paths:
-------------
trunk/src/PyBrainSimGUI.pyw
Copied: trunk/src/PyBrainSim.py (from rev 130, trunk/src/PyBrainSimGUI.pyw)
===================================================================
--- trunk/src/PyBrainSim.py (rev 0)
+++ trunk/src/PyBrainSim.py 2009-09-13 23:40:34 UTC (rev 132)
@@ -0,0 +1,281 @@
+#!/usr/bin/python
+
+# PyBrainSim
+# Copyright 2009 Roman Goj
+#
+# This file is part of PyBrainSim.
+#
+# PyBrainSim is free software: you can redistribute it and/or modify it under
+# the terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# PyBrainSim is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
+# A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# PyBrainSim. If not, see <http://www.gnu.org/licenses/>.
+
+"""
+PyBrainSim is an interactive tool for the simulation and visualization of the
+electromagnetic activity of the brain, currently under development.
+
+This script allows you to choose from a few example simulations, ranging from
+adding numbers to neural mass models.
+
+pybrainsim.sourceforge.net
+"""
+
+import wx
+from wx import xrc
+
+import numpy
+import pylab
+from random import random
+from scipy.integrate import ode
+
+from Head import Head
+from HeadModel import HeadModel
+from HeadModelHalfSphere import HeadModelHalfSphere
+from Experiment import Experiment
+from Stimulus import Stimulus
+from StimulusDummy import StimulusDummy
+from GeneratorDummy import GeneratorDummy
+from GeneratorNumberIncrementing import GeneratorNumberIncrementing
+from GeneratorSine import GeneratorSine
+from Connection import Connection
+from ConnectionDummy import ConnectionDummy
+
+class PyBrainSimGUI(wx.App):
+ def OnInit(self):
+ self.res = xrc.XmlResource("PyBrainSimGUI.xrc")
+
+ self.frame = self.res.LoadFrame(None, "menuFrame")
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton1, id=xrc.XRCID("menuButton1"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton2, id=xrc.XRCID("menuButton2"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton3, id=xrc.XRCID("menuButton3"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton4, id=xrc.XRCID("menuButton4"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton5, id=xrc.XRCID("menuButton5"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton6, id=xrc.XRCID("menuButton6"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton7, id=xrc.XRCID("menuButton7"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButton8, id=xrc.XRCID("menuButton8"))
+ self.frame.Bind(wx.EVT_BUTTON, self.menuButtonQuit, id=xrc.XRCID("menuButtonQuit"))
+ self.frame.Show()
+ return True
+
+ def menuButton1(self, control):
+ exampleHead = Head()
+ exampleHeadModel = HeadModel(exampleHead)
+ exampleHead.setSamplingFrequency(10)
+ exampleHead.addRegistrationSite([0, 0, 0])
+
+ exampleStimulus = StimulusDummy('Stim', exampleHead)
+ exampleGenerator = GeneratorDummy('Gen', exampleHead)
+ exampleConnection = ConnectionDummy('Con', exampleHead, exampleStimulus, exampleGenerator)
+
+ exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 1.0, exampleHead.runSimulation( 1.0 ))
+ print("\nSimulations resulted in the following recording:")
+ print(exampleExperiment.getRecording())
+
+ def menuButton2(self, control):
+ exampleHead = Head()
+ exampleHeadModel = HeadModel(exampleHead)
+ exampleHead.setSamplingFrequency(10)
+ exampleHead.addRegistrationSite([0, 0, 0])
+
+ exampleStimulus = Stimulus('Stim', exampleHead)
+ exampleStimulus.setStimulusTimes([0.3, 0.6])
+ exampleGenerator = GeneratorNumberIncrementing('Gen', exampleHead)
+ exampleConnection = Connection('Con', exampleHead, exampleStimulus, exampleGenerator)
+
+ exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 1.0, exampleHead.runSimulation( 1.0 ))
+ print("\nSimulations resulted in the following recording:")
+ print exampleExperiment.getRecording()
+
+ def menuButton3(self, control):
+ exampleHead = Head()
+ exampleHeadModel = HeadModel(exampleHead)
+ exampleHead.setSamplingFrequency(10)
+ exampleHead.addRegistrationSite([0, 0, 0])
+
+ exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 1.0)
+ exampleExperiment.setStimulusTimes([[0.3, 0.6], [0.5]])
+
+ exampleStimulus1 = Stimulus('Stim1', exampleHead)
+ exampleStimulus2 = Stimulus('Stim2', exampleHead)
+ exampleStimulus1.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
+ exampleStimulus2.setStimulusTimes(exampleExperiment.getStimulusTimes()[1])
+
+ exampleGenerator1 = GeneratorNumberIncrementing('Gen1', exampleHead)
+ exampleGenerator2 = GeneratorNumberIncrementing('Gen2', exampleHead)
+ exampleConnection1 = Connection('Con1', exampleHead, exampleStimulus1, exampleGenerator1)
+ exampleConnection2 = Connection('Con2', exampleHead, exampleStimulus2, exampleGenerator2)
+
+ exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
+ print("\nSimulations resulted in the following recording:")
+ print exampleExperiment.getRecording()
+
+ def menuButton4(self, control):
+ exampleHead = Head()
+ exampleHeadModel = HeadModel(exampleHead)
+ exampleHead.setSamplingFrequency(128)
+ exampleHead.addRegistrationSite([0, 0, 0])
+
+ exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 10.0)
+ exampleExperiment.setStimulusTimes([[0.3, 1.75, 2.16, 3.87, 4.31, 5.183, 6.34, 7.13]])
+
+ exampleStimulus = Stimulus('Stim', exampleHead)
+ print exampleExperiment.getStimulusTimes()[0]
+ exampleStimulus.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
+ exampleGenerator = GeneratorSine('Gen', exampleHead)
+ exampleConnection = Connection('Con', exampleHead, exampleStimulus, exampleGenerator)
+
+ exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
+ print("\nSimulations resulted in the following recording:")
+ print exampleExperiment.getRecording()
+ exampleExperiment.plotRecording()
+
+ def menuButton5(self, control):
+ exampleHead = Head()
+ exampleHeadModel = HeadModel(exampleHead)
+ exampleHead.setSamplingFrequency(256)
+ exampleHead.addRegistrationSite([0, 0, 0])
+ exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 100.0)
+
+ # Randomizing stimuli times
+ stimuli = []
+ for i in range(100):
+ stimuli.append( i + 0.2 +random()/2 )
+ exampleExperiment.setStimulusTimes([stimuli])
+ exampleStimulus = Stimulus('Stim', exampleHead)
+ exampleStimulus.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
+
+ # Creating many generators with random frequencies in the range 2-20 Hz and
+ # random phases. Connecting some of them to the stimulus generator
+ exampleGenerators = []
+ exampleConnections = []
+ for i in range(100):
+ randomFrequency = 2.0 + random() * 18
+ randomPhaseShift = random()
+ exampleGenerators.append(GeneratorSine('Gen', exampleHead, frequency=randomFrequency, phaseShift=randomPhaseShift))
+ if(random() > 0.75):
+ exampleConnections.append(Connection('Con', exampleHead, exampleStimulus, exampleGenerators[i]))
+
+ exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
+ exampleExperiment.plotRecording()
+
+ def menuButton6(self, control):
+ exampleHead = Head()
+ exampleHeadModel = HeadModelHalfSphere(exampleHead)
+ exampleHead.setSamplingFrequency(128)
+ exampleHead.addRegistrationSite([ 0.5, 0.0, 0.866])
+ exampleHead.addRegistrationSite([ 0.0, 0.0, 0.866])
+ exampleHead.addRegistrationSite([-0.5, 0.0, 0.866])
+
+ exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 10.0)
+ exampleExperiment.setStimulusTimes([[0.3, 1.75, 2.16, 3.87, 4.31, 5.183, 6.34, 7.13]])
+
+ exampleStimulus = Stimulus('Stim', exampleHead)
+ exampleStimulus.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
+ exampleGenerator = GeneratorSine('Gen', exampleHead, position = [0.5, 0, 0.366], frequency = 7)
+ exampleGenerator = GeneratorSine('Gen', exampleHead, position = [-0.5, 0, 0.366], frequency = 4)
+ exampleConnection = Connection('Con', exampleHead, exampleStimulus, exampleGenerator)
+
+ exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
+ exampleExperiment.plotRecording()
+
+ def menuButton7(self, control):
+ exampleHead = Head()
+ exampleHead.displayHead()
+
+ def menuButton8(self, control):
+ # This class will produce noise that will be fed into the modelled
+ # cortical area as input.
+ class NeuralNoise:
+ def __init__(self, timeSpan, dtime):
+ self.noise = []
+ self.dtime = dtime
+ self.timePoints = range( int(timeSpan//dtime+1) )
+ # Random noise distributed evenly between 120 and 320
+ for i in self.timePoints:
+ self.noise.append( 120+200*random() )
+ def getNoise(self, time):
+ # Searching for the appropriate time point
+ for i in self.timePoints:
+ if time < self.timePoints[i]*self.dtime:
+ # Interpolating between the randomly generated values
+ return self.noise[i] + \
+ (self.noise[i+1]-self.noise[i]) \
+ * ((self.timePoints[i]*self.dtime-time)/self.dtime)
+
+ # The sigmoid function
+ def sigmoidFunction(v):
+ """Constants taken from Jansen and Rit 1995, p. 360"""
+ cEzero = 2.5
+ cR = 0.56
+ cVzero = 6
+
+ """Sigmoid function taken from Jansen and Rit 1995, p. 360"""
+ return 2*cEzero / ( 1 + numpy.exp( cR*( cVzero - v ) ) )
+
+ # Defining the equations
+ def f(t, y, noiseObject, cC):
+ cA=3.25
+ ca=100.0
+ cB=22.0
+ cb=50.0
+ cC1=cC
+ cC2=0.8*cC1
+ cC3=0.25*cC1
+ cC4=0.25*cC1
+ noise = noiseObject.getNoise(t)
+ #print("Time: " + str(t) + " Noise: " + str(noise))
+ return [y[1], cA*ca*( sigmoidFunction(y[2] - y[4]) ) - 2*ca*y[1] -
+ ca**2*y[0], y[3], cA*ca*( noise + cC2*sigmoidFunction(cC1*y[0]) ) -
+ 2*ca*y[3] - ca**2*y[2], y[5], cB*cb*( cC4*sigmoidFunction(cC3*y[0])
+ ) - 2*cb*y[5] - cb**2*y[4]]
+
+ # How many seconds should be modelled and how accurately
+ timeSpan = 3
+ dtime = 0.001
+ firstPointToDisplay = 1000
+
+ # A noise object that will be used as input into the equations
+ someNoise = NeuralNoise(timeSpan+1, dtime*10)
+
+ # Initial conditions
+ y0, t0 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0
+
+ # Preparing for simulations
+ r = []
+ recording = [ [], [], [], [], [], [] ]
+ parameter = [ 68, 128, 135, 270, 675, 1350 ]
+
+ # Differential equation integration
+ for i in range(len(parameter)):
+ # Preparing the integration
+ r.append(ode(f).set_integrator('vode', method='bdf'))
+ r[i].set_initial_value(y0, t0)
+ r[i].set_f_params( someNoise, parameter[i] )
+ # Integrating
+ print("Performing simulation " + str(i+1) + "/" + str(len(parameter)))
+ while r[i].successful() and r[i].t < timeSpan:
+ r[i].integrate(r[i].t+dtime)
+ recording[i].append(r[i].y[2] - r[i].y[4])
+
+ # Show results of simulations
+ for i in range(len(parameter)):
+ pylab.subplot( len(parameter), 1, i+1)
+ if i==0:
+ pylab.title("Simulations based on Fig. 3 from Jansen and Rit 1995")
+ pylab.ylabel( "C = " + str(parameter[i]) )
+ pylab.plot(recording[i][firstPointToDisplay:])
+ pylab.show()
+
+ def menuButtonQuit(self, control):
+ self.Exit()
+
+if __name__ == '__main__':
+ app = PyBrainSimGUI(0)
+ app.MainLoop()
Property changes on: trunk/src/PyBrainSim.py
___________________________________________________________________
Added: svn:executable
+ *
Added: svn:mergeinfo
+
Deleted: trunk/src/PyBrainSimGUI.pyw
===================================================================
--- trunk/src/PyBrainSimGUI.pyw 2009-09-13 23:39:27 UTC (rev 131)
+++ trunk/src/PyBrainSimGUI.pyw 2009-09-13 23:40:34 UTC (rev 132)
@@ -1,281 +0,0 @@
-#!/usr/bin/python
-
-# PyBrainSim
-# Copyright 2009 Roman Goj
-#
-# This file is part of PyBrainSim.
-#
-# PyBrainSim is free software: you can redistribute it and/or modify it under
-# the terms of the GNU General Public License as published by the Free Software
-# Foundation, either version 3 of the License, or (at your option) any later
-# version.
-#
-# PyBrainSim is distributed in the hope that it will be useful, but WITHOUT ANY
-# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
-# A PARTICULAR PURPOSE. See the GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License along with
-# PyBrainSim. If not, see <http://www.gnu.org/licenses/>.
-
-"""
-PyBrainSim is an interactive tool for the simulation and visualization of the
-electromagnetic activity of the brain, currently under development.
-
-This script allows you to choose from a few example simulations, ranging from
-adding numbers to neural mass models.
-
-pybrainsim.sourceforge.net
-"""
-
-import wx
-from wx import xrc
-
-import numpy
-import pylab
-from random import random
-from scipy.integrate import ode
-
-from Head import Head
-from HeadModel import HeadModel
-from HeadModelHalfSphere import HeadModelHalfSphere
-from Experiment import Experiment
-from Stimulus import Stimulus
-from StimulusDummy import StimulusDummy
-from GeneratorDummy import GeneratorDummy
-from GeneratorNumberIncrementing import GeneratorNumberIncrementing
-from GeneratorSine import GeneratorSine
-from Connection import Connection
-from ConnectionDummy import ConnectionDummy
-
-class PyBrainSimGUI(wx.App):
- def OnInit(self):
- self.res = xrc.XmlResource("PyBrainSimGUI.xrc")
-
- self.frame = self.res.LoadFrame(None, "menuFrame")
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton1, id=xrc.XRCID("menuButton1"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton2, id=xrc.XRCID("menuButton2"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton3, id=xrc.XRCID("menuButton3"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton4, id=xrc.XRCID("menuButton4"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton5, id=xrc.XRCID("menuButton5"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton6, id=xrc.XRCID("menuButton6"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton7, id=xrc.XRCID("menuButton7"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButton8, id=xrc.XRCID("menuButton8"))
- self.frame.Bind(wx.EVT_BUTTON, self.menuButtonQuit, id=xrc.XRCID("menuButtonQuit"))
- self.frame.Show()
- return True
-
- def menuButton1(self, control):
- exampleHead = Head()
- exampleHeadModel = HeadModel(exampleHead)
- exampleHead.setSamplingFrequency(10)
- exampleHead.addRegistrationSite([0, 0, 0])
-
- exampleStimulus = StimulusDummy('Stim', exampleHead)
- exampleGenerator = GeneratorDummy('Gen', exampleHead)
- exampleConnection = ConnectionDummy('Con', exampleHead, exampleStimulus, exampleGenerator)
-
- exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 1.0, exampleHead.runSimulation( 1.0 ))
- print("\nSimulations resulted in the following recording:")
- print(exampleExperiment.getRecording())
-
- def menuButton2(self, control):
- exampleHead = Head()
- exampleHeadModel = HeadModel(exampleHead)
- exampleHead.setSamplingFrequency(10)
- exampleHead.addRegistrationSite([0, 0, 0])
-
- exampleStimulus = Stimulus('Stim', exampleHead)
- exampleStimulus.setStimulusTimes([0.3, 0.6])
- exampleGenerator = GeneratorNumberIncrementing('Gen', exampleHead)
- exampleConnection = Connection('Con', exampleHead, exampleStimulus, exampleGenerator)
-
- exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 1.0, exampleHead.runSimulation( 1.0 ))
- print("\nSimulations resulted in the following recording:")
- print exampleExperiment.getRecording()
-
- def menuButton3(self, control):
- exampleHead = Head()
- exampleHeadModel = HeadModel(exampleHead)
- exampleHead.setSamplingFrequency(10)
- exampleHead.addRegistrationSite([0, 0, 0])
-
- exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 1.0)
- exampleExperiment.setStimulusTimes([[0.3, 0.6], [0.5]])
-
- exampleStimulus1 = Stimulus('Stim1', exampleHead)
- exampleStimulus2 = Stimulus('Stim2', exampleHead)
- exampleStimulus1.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
- exampleStimulus2.setStimulusTimes(exampleExperiment.getStimulusTimes()[1])
-
- exampleGenerator1 = GeneratorNumberIncrementing('Gen1', exampleHead)
- exampleGenerator2 = GeneratorNumberIncrementing('Gen2', exampleHead)
- exampleConnection1 = Connection('Con1', exampleHead, exampleStimulus1, exampleGenerator1)
- exampleConnection2 = Connection('Con2', exampleHead, exampleStimulus2, exampleGenerator2)
-
- exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
- print("\nSimulations resulted in the following recording:")
- print exampleExperiment.getRecording()
-
- def menuButton4(self, control):
- exampleHead = Head()
- exampleHeadModel = HeadModel(exampleHead)
- exampleHead.setSamplingFrequency(128)
- exampleHead.addRegistrationSite([0, 0, 0])
-
- exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 10.0)
- exampleExperiment.setStimulusTimes([[0.3, 1.75, 2.16, 3.87, 4.31, 5.183, 6.34, 7.13]])
-
- exampleStimulus = Stimulus('Stim', exampleHead)
- print exampleExperiment.getStimulusTimes()[0]
- exampleStimulus.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
- exampleGenerator = GeneratorSine('Gen', exampleHead)
- exampleConnection = Connection('Con', exampleHead, exampleStimulus, exampleGenerator)
-
- exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
- print("\nSimulations resulted in the following recording:")
- print exampleExperiment.getRecording()
- exampleExperiment.plotRecording()
-
- def menuButton5(self, control):
- exampleHead = Head()
- exampleHeadModel = HeadModel(exampleHead)
- exampleHead.setSamplingFrequency(256)
- exampleHead.addRegistrationSite([0, 0, 0])
- exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 100.0)
-
- # Randomizing stimuli times
- stimuli = []
- for i in range(100):
- stimuli.append( i + 0.2 +random()/2 )
- exampleExperiment.setStimulusTimes([stimuli])
- exampleStimulus = Stimulus('Stim', exampleHead)
- exampleStimulus.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
-
- # Creating many generators with random frequencies in the range 2-20 Hz and
- # random phases. Connecting some of them to the stimulus generator
- exampleGenerators = []
- exampleConnections = []
- for i in range(100):
- randomFrequency = 2.0 + random() * 18
- randomPhaseShift = random()
- exampleGenerators.append(GeneratorSine('Gen', exampleHead, frequency=randomFrequency, phaseShift=randomPhaseShift))
- if(random() > 0.75):
- exampleConnections.append(Connection('Con', exampleHead, exampleStimulus, exampleGenerators[i]))
-
- exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
- exampleExperiment.plotRecording()
-
- def menuButton6(self, control):
- exampleHead = Head()
- exampleHeadModel = HeadModelHalfSphere(exampleHead)
- exampleHead.setSamplingFrequency(128)
- exampleHead.addRegistrationSite([ 0.5, 0.0, 0.866])
- exampleHead.addRegistrationSite([ 0.0, 0.0, 0.866])
- exampleHead.addRegistrationSite([-0.5, 0.0, 0.866])
-
- exampleExperiment = Experiment(exampleHead.getSamplingFrequency(), 10.0)
- exampleExperiment.setStimulusTimes([[0.3, 1.75, 2.16, 3.87, 4.31, 5.183, 6.34, 7.13]])
-
- exampleStimulus = Stimulus('Stim', exampleHead)
- exampleStimulus.setStimulusTimes(exampleExperiment.getStimulusTimes()[0])
- exampleGenerator = GeneratorSine('Gen', exampleHead, position = [0.5, 0, 0.366], frequency = 7)
- exampleGenerator = GeneratorSine('Gen', exampleHead, position = [-0.5, 0, 0.366], frequency = 4)
- exampleConnection = Connection('Con', exampleHead, exampleStimulus, exampleGenerator)
-
- exampleExperiment.setRecording(exampleHead.runSimulation(exampleExperiment.getDuration()))
- exampleExperiment.plotRecording()
-
- def menuButton7(self, control):
- exampleHead = Head()
- exampleHead.displayHead()
-
- def menuButton8(self, control):
- # This class will produce noise that will be fed into the modelled
- # cortical area as input.
- class NeuralNoise:
- def __init__(self, timeSpan, dtime):
- self.noise = []
- self.dtime = dtime
- self.timePoints = range( int(timeSpan//dtime+1) )
- # Random noise distributed evenly between 120 and 320
- for i in self.timePoints:
- self.noise.append( 120+200*random() )
- def getNoise(self, time):
- # Searching for the appropriate time point
- for i in self.timePoints:
- if time < self.timePoints[i]*self.dtime:
- # Interpolating between the randomly generated values
- return self.noise[i] + \
- (self.noise[i+1]-self.noise[i]) \
- * ((self.timePoints[i]*self.dtime-time)/self.dtime)
-
- # The sigmoid function
- def sigmoidFunction(v):
- """Constants taken from Jansen and Rit 1995, p. 360"""
- cEzero = 2.5
- cR = 0.56
- cVzero = 6
-
- """Sigmoid function taken from Jansen and Rit 1995, p. 360"""
- return 2*cEzero / ( 1 + numpy.exp( cR*( cVzero - v ) ) )
-
- # Defining the equations
- def f(t, y, noiseObject, cC):
- cA=3.25
- ca=100.0
- cB=22.0
- cb=50.0
- cC1=cC
- cC2=0.8*cC1
- cC3=0.25*cC1
- cC4=0.25*cC1
- noise = noiseObject.getNoise(t)
- #print("Time: " + str(t) + " Noise: " + str(noise))
- return [y[1], cA*ca*( sigmoidFunction(y[2] - y[4]) ) - 2*ca*y[1] -
- ca**2*y[0], y[3], cA*ca*( noise + cC2*sigmoidFunction(cC1*y[0]) ) -
- 2*ca*y[3] - ca**2*y[2], y[5], cB*cb*( cC4*sigmoidFunction(cC3*y[0])
- ) - 2*cb*y[5] - cb**2*y[4]]
-
- # How many seconds should be modelled and how accurately
- timeSpan = 3
- dtime = 0.001
- firstPointToDisplay = 1000
-
- # A noise object that will be used as input into the equations
- someNoise = NeuralNoise(timeSpan+1, dtime*10)
-
- # Initial conditions
- y0, t0 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0
-
- # Preparing for simulations
- r = []
- recording = [ [], [], [], [], [], [] ]
- parameter = [ 68, 128, 135, 270, 675, 1350 ]
-
- # Differential equation integration
- for i in range(len(parameter)):
- # Preparing the integration
- r.append(ode(f).set_integrator('vode', method='bdf'))
- r[i].set_initial_value(y0, t0)
- r[i].set_f_params( someNoise, parameter[i] )
- # Integrating
- print("Performing simulation " + str(i+1) + "/" + str(len(parameter)))
- while r[i].successful() and r[i].t < timeSpan:
- r[i].integrate(r[i].t+dtime)
- recording[i].append(r[i].y[2] - r[i].y[4])
-
- # Show results of simulations
- for i in range(len(parameter)):
- pylab.subplot( len(parameter), 1, i+1)
- if i==0:
- pylab.title("Simulations based on Fig. 3 from Jansen and Rit 1995")
- pylab.ylabel( "C = " + str(parameter[i]) )
- pylab.plot(recording[i][firstPointToDisplay:])
- pylab.show()
-
- def menuButtonQuit(self, control):
- self.Exit()
-
-if __name__ == '__main__':
- app = PyBrainSimGUI(0)
- app.MainLoop()
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