def __init__(self, conf, pool, index, kind, muscleThickness,
skinThickness):
'''
Constructor
- Inputs:
+ **conf**: Configuration object with the simulation parameters.
+ **pool**: string with Motor unit pool to which the motor
unit belongs.
+ **index**: integer corresponding to the motor unit order in
the pool, according to the Henneman's principle (size principle).
+ **kind**: string with the type of the motor unit. It can
be *S* (slow), *FR* (fast and resistant), and
*FF* (fast and fatigable).
'''
## Configuration object with the simulation parameters.
self.conf = conf
## String with the type of the motor unit. It can be
## *S* (slow), *FR* (fast and resistant) and
## *FF** (fast and fatigable).
self.kind = kind
self.pool = pool
# Neural compartments
## The instant of the last spike of the Motor unit
## at the Soma compartment.
self.tSomaSpike = float("-inf")
NumberOfAxonNodes = int(
conf.parameterSet('NumberAxonNodes', pool, index))
compartmentsList = ['dendrite', 'soma']
for i in xrange(0, NumberOfAxonNodes):
compartmentsList.append('internode')
compartmentsList.append('node')
## Integer corresponding to the motor unit order in the pool, according to the Henneman's principle (size principle).
self.index = int(index)
## Dictionary of Compartment of the Motor Unit.
self.compartment = dict()
## Value of the membrane potential, in mV, that is considered a spike.
self.threshold_mV = conf.parameterSet('threshold', pool, index)
## Anatomical position of the neuron, in mm.
self.position_mm = conf.parameterSet('position', pool, index)
# EMG data
self.MUSpatialDistribution = conf.parameterSet('MUSpatialDistribution',
pool, 0)
if self.MUSpatialDistribution == 'random':
radius = (muscleThickness / 2) * np.random.uniform(0.0, 1.0)
angle = 2.0 * math.pi * np.random.uniform(0.0, 1.0)
x = radius * math.sin(angle)
y = radius * math.cos(angle)
## Anatomical coordinate of the muscle unit in a muscle section, in (mm,mm).
self.muSectionPosition_mm = [x, y]
## Distance of the MU to the EMG elctrode, in mm.
self.distance_mm = math.sqrt((x + muscleThickness / 2.0 +
skinThickness)**2 + y**2)
## Attenuation of the MUAP amplitude, as measured in the electrode.
self.attenuationToSkin = math.exp(-self.distance_mm /
conf.EMGAttenuation_mm1)
## Widening of the MUAP duration, as measured in the electrode.
self.timeWidening = 1 + conf.EMGWidening_mm1 * self.distance_mm
## Type of the Hermitez-Rodiguez curve. It can be 1 or 2.
self.hrType = np.random.random_integers(1, 2)
## MUAP amplitude in mV.
self.ampEMG_mV = conf.parameterSet('EMGAmplitude', pool, index)
self.ampEMG_mV = self.ampEMG_mV * self.attenuationToSkin
## MUAP time constant, in ms.
self.timeCteEMG_ms = conf.parameterSet('EMGDuration', pool, index)
self.timeCteEMG_ms = self.timeCteEMG_ms * self.timeWidening
for i in xrange(len(compartmentsList)):
self.compartment[i] = Compartment(compartmentsList[i], conf, pool,
index, self.kind)
## Number of compartments.
self.compNumber = len(self.compartment)
## Vector with membrane potential,in mV, of all compartments.
self.v_mV = np.zeros((self.compNumber), dtype=np.float64)
## Vector with the last instant of spike of all compartments.
self.tSpikes = np.zeros((self.compNumber), dtype=np.float64)
gCoupling_muS = np.zeros_like(self.v_mV, dtype='d')
for i in xrange(len(self.compartment) - 1):
gCoupling_muS[i] = calcGCoupling(
float(conf.parameterSet('cytR', pool, index)),
self.compartment[i].length_mum,
self.compartment[i + 1].length_mum,
self.compartment[i].diameter_mum,
self.compartment[i + 1].diameter_mum)
gLeak = np.zeros_like(self.v_mV, dtype='d')
capacitance_nF = np.zeros_like(self.v_mV, dtype='d')
EqPot = np.zeros_like(self.v_mV, dtype='d')
IPump = np.zeros_like(self.v_mV, dtype='d')
compLength = np.zeros_like(self.v_mV, dtype='d')
for i in xrange(len(self.compartment)):
capacitance_nF[i] = self.compartment[i].capacitance_nF
gLeak[i] = self.compartment[i].gLeak_muS
EqPot[i] = self.compartment[i].EqPot_mV
IPump[i] = self.compartment[i].IPump_nA
compLength[i] = self.compartment[i].length_mum
self.v_mV[i] = self.compartment[i].EqPot_mV
## Vector with the inverse of the capacitance of all compartments.
self.capacitanceInv = 1.0 / capacitance_nF
## Vector with current, in nA, of each compartment coming from other elements of the model. For example
## from ionic channels and synapses.
self.iIonic = np.full_like(self.v_mV, 0.0)
## Vector with the current, in nA, injected in each compartment.
self.iInjected = np.zeros_like(self.v_mV, dtype='d')
#self.iInjected = np.array([0, 10.0])
GC = compGCouplingMatrix(gCoupling_muS)
GL = -np.diag(gLeak)
## Matrix of the conductance of the motoneuron. Multiplied by the vector self.v_mV,
## results in the passive currents of each compartment.
self.G = np.float64(GC + GL)
self.EqCurrent_nA = np.dot(-GL, EqPot) + IPump
## index of the soma compartment.
self.somaIndex = compartmentsList.index('soma')
## index of the last compartment.
self.lastCompIndex = self.compNumber - 1
## Refractory period, in ms, of the motoneuron.
self.MNRefPer_ms = float(conf.parameterSet('MNSomaRefPer', pool,
index))
# delay
## String with type of the nerve. It can be PTN (posterior tibial nerve) or CPN
## (common peroneal nerve).
if pool == 'SOL' or pool == 'MG' or pool == 'LG':
self.nerve = 'PTN'
elif pool == 'TA':
self.nerve = 'CPN'
## Distance, in m, of the stimulus position to the terminal.
self.stimulusPositiontoTerminal = float(
conf.parameterSet('stimDistToTerm_' + self.nerve, pool, index))
## AxonDelay object of the motor unit.
if NumberOfAxonNodes == 0:
dynamicNerveLength = 0
else:
dynamicNerveLength = np.sum(compLength[2:-1]) * 1e-6
self.nerveLength = float(
conf.parameterSet('nerveLength_' + self.nerve, pool, index))
delayLength = self.nerveLength - dynamicNerveLength
if self.stimulusPositiontoTerminal < delayLength:
self.Delay = AxonDelay(conf, self.nerve, pool, delayLength,
self.stimulusPositiontoTerminal, index)
self.stimulusCompartment = 'delay'
else:
self.Delay = AxonDelay(conf, self.nerve, pool, delayLength, -1,
index)
self.stimulusCompartment = -1
# Nerve stimulus function
self.stimulusMeanFrequency_Hz = float(
conf.parameterSet('stimFrequency_' + self.nerve, pool, 0))
self.stimulusPulseDuration_ms = float(
conf.parameterSet('stimPulseDuration_' + self.nerve, pool, 0))
self.stimulusIntensity_mA = float(
conf.parameterSet('stimIntensity_' + self.nerve, pool, 0))
self.stimulusStart_ms = float(
conf.parameterSet('stimStart_' + self.nerve, pool, 0))
self.stimulusStop_ms = float(
conf.parameterSet('stimStop_' + self.nerve, pool, 0))
self.stimulusModulationStart_ms = float(
conf.parameterSet('stimModulationStart_' + self.nerve, pool, 0))
self.stimulusModulationStop_ms = float(
conf.parameterSet('stimModulationStop_' + self.nerve, pool, 0))
exec 'def axonStimModulation(t): return ' + conf.parameterSet(
'stimModulation_' + self.nerve, pool, 0)
startStep = int(np.rint(self.stimulusStart_ms / self.conf.timeStep_ms))
self.axonStimModulation = axonStimModulation
## Vector with the nerve stimulus, in mA.
self.nerveStimulus_mA = np.zeros(
(int(np.rint(conf.simDuration_ms / conf.timeStep_ms)), 1),
dtype=float)
for i in xrange(len(self.nerveStimulus_mA)):
if (i * self.conf.timeStep_ms >= self.stimulusStart_ms
and i * self.conf.timeStep_ms <= self.stimulusStop_ms):
if (i * self.conf.timeStep_ms > self.stimulusModulationStart_ms
and i * self.conf.timeStep_ms <
self.stimulusModulationStop_ms):
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz + axonStimModulation(
i * self.conf.timeStep_ms)
else:
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz
if stimulusFrequency_Hz > 0:
stimulusPeriod_ms = 1000.0 / stimulusFrequency_Hz
numberOfSteps = int(
np.rint(stimulusPeriod_ms / self.conf.timeStep_ms))
if ((i - startStep) % numberOfSteps == 0):
self.nerveStimulus_mA[i:int(
np.rint(i + self.stimulusPulseDuration_ms /
self.conf.timeStep_ms)
)] = self.stimulusIntensity_mA
#
## Vector with the instants of spikes at the soma.
self.somaSpikeTrain = []
## Vector with the instants of spikes at the last compartment.
self.lastCompSpikeTrain = []
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
# contraction DataMUnumber_S = int(conf.parameterSet('MUnumber_S_' + pool, pool, 0))
activationModel = conf.parameterSet('activationModel', pool, 0)
## Contraction time of the twitch muscle unit, in ms.
self.TwitchTc_ms = conf.parameterSet('twitchTimePeak', pool, index)
## Amplitude of the muscle unit twitch, in N.
self.TwitchAmp_N = conf.parameterSet('twitchPeak', pool, index)
## Parameter of the saturation.
self.bSat = conf.parameterSet('bSat' + activationModel, pool, index)
## Twitch- tetanus relationship
self.twTet = conf.parameterSet('twTet' + activationModel, pool, index)
## EMG data
## Build synapses
self.SynapsesOut = []
self.transmitSpikesThroughSynapses = []
self.indicesOfSynapsesOnTarget = []
def __init__(self, conf, pool, muscle, index):
'''
Constructor
- Inputs:
+ **conf**: Configuration object with the simulation parameters.
+ **pool**: string with Motor unit pool to which the motor
unit belongs.
+ **muscle**:
+ **index**: integer corresponding to the motor unit order in
the pool, according to the Henneman's principle (size principle).
'''
## Configuration object with the simulation parameters.
self.conf = conf
self.timeStep_ms = self.conf.timeStep_ms
self.timeStepByTwo_ms = self.conf.timeStepByTwo_ms
self.timeStepBySix_ms = self.conf.timeStepBySix_ms
self.kind = muscle
self.muscle = muscle
# Neural compartments
self.pool = pool
NumberOfAxonNodes = int(conf.parameterSet('NumberAxonNodes', pool, index))
compartmentsList = []
for i in xrange(0, NumberOfAxonNodes):
compartmentsList.append('internode')
compartmentsList.append('node')
## Integer corresponding to the motor unit order in the pool, according to the Henneman's principle (size principle).
self.index = int(index)
## Dictionary of Compartment of the Motor Unit.
self.compartment = dict()
for i in xrange(len(compartmentsList)):
self.compartment[i] = Compartment(compartmentsList[i], conf, pool, index, self.kind)
## Number of compartments.
self.compNumber = len(self.compartment)
## Value of the membrane potential, in mV, that is considered a spike.
if self.compNumber:
self.threshold_mV = conf.parameterSet('threshold', pool + '-' + muscle, index)
else:
self.threshold_mV = 0
## Vector with membrane potential,in mV, of all compartments.
self.v_mV = np.zeros((self.compNumber), dtype = np.float64)
## Vector with the last instant of spike of all compartments.
self.tSpikes = np.zeros((self.compNumber), dtype = np.float64)
gCoupling_muS = np.zeros_like(self.v_mV, dtype = 'd')
for i in xrange(len(self.compartment)-1):
gCoupling_muS[i] = calcGCoupling(float(conf.parameterSet('cytR',pool, index)),
self.compartment[i].length_mum,
self.compartment[i + 1].length_mum,
self.compartment[i].diameter_mum,
self.compartment[i + 1].diameter_mum)
gLeak = np.zeros_like(self.v_mV, dtype = 'd')
capacitance_nF = np.zeros_like(self.v_mV, dtype = 'd')
EqPot = np.zeros_like(self.v_mV, dtype = 'd')
IPump = np.zeros_like(self.v_mV, dtype = 'd')
compLength = np.zeros_like(self.v_mV, dtype = 'd')
for i in xrange(len(self.compartment)):
capacitance_nF[i] = self.compartment[i].capacitance_nF
gLeak[i] = self.compartment[i].gLeak_muS
EqPot[i] = self.compartment[i].EqPot_mV
IPump[i] = self.compartment[i].IPump_nA
compLength[i] = self.compartment[i].length_mum
self.v_mV[i] = self.compartment[i].EqPot_mV
## Vector with the inverse of the capacitance of all compartments.
self.capacitanceInv = 1.0 / capacitance_nF
## Vector with current, in nA, of each compartment coming from other elements of the model. For example
## from ionic channels and synapses.
self.iIonic = np.full_like(self.v_mV, 0.0)
## Vector with the current, in nA, injected in each compartment.
self.iInjected = np.zeros_like(self.v_mV, dtype = 'd')
#self.iInjected = np.array([0, 10.0])
GC = compGCouplingMatrix(gCoupling_muS)
GL = -np.diag(gLeak)
## Matrix of the conductance of the motoneuron. Multiplied by the vector self.v_mV,
## results in the passive currents of each compartment.
self.G = np.float64(GC + GL)
self.EqCurrent_nA = np.dot(-GL, EqPot) + IPump
## index of the last compartment.
self.lastCompIndex = self.compNumber - 1
## Refractory period, in ms, of the motoneuron.
self.AFRefPer_ms = float(conf.parameterSet('AFRefPer', pool, index))
# delay
## String with type of the nerve. It can be PTN (posterior tibial nerve) or CPN
## (common peroneal nerve).
if self.muscle == 'SOL' or self.muscle == 'MG' or self.muscle == 'LG':
self.nerve = 'PTN'
elif self.muscle == 'TA':
self.nerve = 'CPN'
## AxonDelay object of the motor unit.
if NumberOfAxonNodes == 0:
dynamicNerveLength = 0
else:
dynamicNerveLength = np.sum(compLength[2:-1]) * 1e-6
self.nerveLength = float(conf.parameterSet('nerveLength_' + self.nerve, pool, index))
## Distance, in m, of the stimulus position to the terminal.
self.stimulusPositiontoTerminal = self.nerveLength - float(conf.parameterSet('stimDistToTerm_' + self.nerve, pool, index))
##Frequency threshold of the afferent to th proprioceptor input
self.frequencyThreshold_Hz = float(conf.parameterSet('frequencyThreshold',
pool + '-' + muscle, index))
delayLength = self.nerveLength - dynamicNerveLength
if self.stimulusPositiontoTerminal < delayLength:
self.Delay = AxonDelay(conf, self.nerve, pool + '-' + self.muscle, delayLength, self.stimulusPositiontoTerminal, index)
self.stimulusCompartment = 'delay'
else:
self.Delay = AxonDelay(conf, self.nerve, pool + '-' + self.muscle, delayLength, -1, index)
self.stimulusCompartment = -1
# Nerve stimulus function
self.stimulusMeanFrequency_Hz = float(conf.parameterSet('stimFrequency_' + self.nerve, pool, 0))
self.stimulusPulseDuration_ms = float(conf.parameterSet('stimPulseDuration_' + self.nerve, pool, 0))
self.stimulusIntensity_mA = float(conf.parameterSet('stimIntensity_' + self.nerve, pool, 0))
self.stimulusStart_ms = float(conf.parameterSet('stimStart_' + self.nerve, pool, 0))
self.stimulusStop_ms = float(conf.parameterSet('stimStop_' + self.nerve, pool, 0))
self.stimulusModulationStart_ms = float(conf.parameterSet('stimModulationStart_' + self.nerve, pool, 0))
self.stimulusModulationStop_ms = float(conf.parameterSet('stimModulationStop_' + self.nerve, pool, 0))
exec 'def axonStimModulation(t): return ' + conf.parameterSet('stimModulation_' + self.nerve, pool, 0)
self.axonStimModulation = axonStimModulation
## Vector with the nerve stimulus, in mA.
self.nerveStimulus_mA = np.zeros((int(np.rint(conf.simDuration_ms/conf.timeStep_ms)), 1), dtype = float)
self.createStimulus()
#
## Vector with the instants of spikes at the last compartment.
self.lastCompSpikeTrain = []
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
self.GammaOrder = int(conf.parameterSet('GammaOrder_' + self.pool + '-' + self.muscle, pool, 0))
## A PointProcessGenerator object, corresponding the generator of
## spikes of the neural tract unit.
self.spikesGenerator = PointProcessGenerator(index)
self.proprioceptorSpikeTrain = self.spikesGenerator.points
## Build synapses
self.SynapsesOut = []
self.transmitSpikesThroughSynapses = []
self.indicesOfSynapsesOnTarget = []
class AfferentUnit(object):
'''
Class that implements a motor unit model. Encompasses a motoneuron
and a muscle unit.
'''
def __init__(self, conf, pool, muscle, index):
'''
Constructor
- Inputs:
+ **conf**: Configuration object with the simulation parameters.
+ **pool**: string with Motor unit pool to which the motor
unit belongs.
+ **muscle**:
+ **index**: integer corresponding to the motor unit order in
the pool, according to the Henneman's principle (size principle).
'''
## Configuration object with the simulation parameters.
self.conf = conf
self.timeStep_ms = self.conf.timeStep_ms
self.timeStepByTwo_ms = self.conf.timeStepByTwo_ms
self.timeStepBySix_ms = self.conf.timeStepBySix_ms
self.kind = muscle
self.muscle = muscle
# Neural compartments
self.pool = pool
NumberOfAxonNodes = int(conf.parameterSet('NumberAxonNodes', pool, index))
compartmentsList = []
for i in xrange(0, NumberOfAxonNodes):
compartmentsList.append('internode')
compartmentsList.append('node')
## Integer corresponding to the motor unit order in the pool, according to the Henneman's principle (size principle).
self.index = int(index)
## Dictionary of Compartment of the Motor Unit.
self.compartment = dict()
for i in xrange(len(compartmentsList)):
self.compartment[i] = Compartment(compartmentsList[i], conf, pool, index, self.kind)
## Number of compartments.
self.compNumber = len(self.compartment)
## Value of the membrane potential, in mV, that is considered a spike.
if self.compNumber:
self.threshold_mV = conf.parameterSet('threshold', pool + '-' + muscle, index)
else:
self.threshold_mV = 0
## Vector with membrane potential,in mV, of all compartments.
self.v_mV = np.zeros((self.compNumber), dtype = np.float64)
## Vector with the last instant of spike of all compartments.
self.tSpikes = np.zeros((self.compNumber), dtype = np.float64)
gCoupling_muS = np.zeros_like(self.v_mV, dtype = 'd')
for i in xrange(len(self.compartment)-1):
gCoupling_muS[i] = calcGCoupling(float(conf.parameterSet('cytR',pool, index)),
self.compartment[i].length_mum,
self.compartment[i + 1].length_mum,
self.compartment[i].diameter_mum,
self.compartment[i + 1].diameter_mum)
gLeak = np.zeros_like(self.v_mV, dtype = 'd')
capacitance_nF = np.zeros_like(self.v_mV, dtype = 'd')
EqPot = np.zeros_like(self.v_mV, dtype = 'd')
IPump = np.zeros_like(self.v_mV, dtype = 'd')
compLength = np.zeros_like(self.v_mV, dtype = 'd')
for i in xrange(len(self.compartment)):
capacitance_nF[i] = self.compartment[i].capacitance_nF
gLeak[i] = self.compartment[i].gLeak_muS
EqPot[i] = self.compartment[i].EqPot_mV
IPump[i] = self.compartment[i].IPump_nA
compLength[i] = self.compartment[i].length_mum
self.v_mV[i] = self.compartment[i].EqPot_mV
## Vector with the inverse of the capacitance of all compartments.
self.capacitanceInv = 1.0 / capacitance_nF
## Vector with current, in nA, of each compartment coming from other elements of the model. For example
## from ionic channels and synapses.
self.iIonic = np.full_like(self.v_mV, 0.0)
## Vector with the current, in nA, injected in each compartment.
self.iInjected = np.zeros_like(self.v_mV, dtype = 'd')
#self.iInjected = np.array([0, 10.0])
GC = compGCouplingMatrix(gCoupling_muS)
GL = -np.diag(gLeak)
## Matrix of the conductance of the motoneuron. Multiplied by the vector self.v_mV,
## results in the passive currents of each compartment.
self.G = np.float64(GC + GL)
self.EqCurrent_nA = np.dot(-GL, EqPot) + IPump
## index of the last compartment.
self.lastCompIndex = self.compNumber - 1
## Refractory period, in ms, of the motoneuron.
self.AFRefPer_ms = float(conf.parameterSet('AFRefPer', pool, index))
# delay
## String with type of the nerve. It can be PTN (posterior tibial nerve) or CPN
## (common peroneal nerve).
if self.muscle == 'SOL' or self.muscle == 'MG' or self.muscle == 'LG':
self.nerve = 'PTN'
elif self.muscle == 'TA':
self.nerve = 'CPN'
## AxonDelay object of the motor unit.
if NumberOfAxonNodes == 0:
dynamicNerveLength = 0
else:
dynamicNerveLength = np.sum(compLength[2:-1]) * 1e-6
self.nerveLength = float(conf.parameterSet('nerveLength_' + self.nerve, pool, index))
## Distance, in m, of the stimulus position to the terminal.
self.stimulusPositiontoTerminal = self.nerveLength - float(conf.parameterSet('stimDistToTerm_' + self.nerve, pool, index))
##Frequency threshold of the afferent to th proprioceptor input
self.frequencyThreshold_Hz = float(conf.parameterSet('frequencyThreshold',
pool + '-' + muscle, index))
delayLength = self.nerveLength - dynamicNerveLength
if self.stimulusPositiontoTerminal < delayLength:
self.Delay = AxonDelay(conf, self.nerve, pool + '-' + self.muscle, delayLength, self.stimulusPositiontoTerminal, index)
self.stimulusCompartment = 'delay'
else:
self.Delay = AxonDelay(conf, self.nerve, pool + '-' + self.muscle, delayLength, -1, index)
self.stimulusCompartment = -1
# Nerve stimulus function
self.stimulusMeanFrequency_Hz = float(conf.parameterSet('stimFrequency_' + self.nerve, pool, 0))
self.stimulusPulseDuration_ms = float(conf.parameterSet('stimPulseDuration_' + self.nerve, pool, 0))
self.stimulusIntensity_mA = float(conf.parameterSet('stimIntensity_' + self.nerve, pool, 0))
self.stimulusStart_ms = float(conf.parameterSet('stimStart_' + self.nerve, pool, 0))
self.stimulusStop_ms = float(conf.parameterSet('stimStop_' + self.nerve, pool, 0))
self.stimulusModulationStart_ms = float(conf.parameterSet('stimModulationStart_' + self.nerve, pool, 0))
self.stimulusModulationStop_ms = float(conf.parameterSet('stimModulationStop_' + self.nerve, pool, 0))
exec 'def axonStimModulation(t): return ' + conf.parameterSet('stimModulation_' + self.nerve, pool, 0)
self.axonStimModulation = axonStimModulation
## Vector with the nerve stimulus, in mA.
self.nerveStimulus_mA = np.zeros((int(np.rint(conf.simDuration_ms/conf.timeStep_ms)), 1), dtype = float)
self.createStimulus()
#
## Vector with the instants of spikes at the last compartment.
self.lastCompSpikeTrain = []
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
self.GammaOrder = int(conf.parameterSet('GammaOrder_' + self.pool + '-' + self.muscle, pool, 0))
## A PointProcessGenerator object, corresponding the generator of
## spikes of the neural tract unit.
self.spikesGenerator = PointProcessGenerator(index)
self.proprioceptorSpikeTrain = self.spikesGenerator.points
## Build synapses
self.SynapsesOut = []
self.transmitSpikesThroughSynapses = []
self.indicesOfSynapsesOnTarget = []
def atualizeAfferentUnit(self, t, proprioceptorFR):
'''
Atualize the dynamical and nondynamical (delay) parts of the motor unit.
- Inputs:
+ **t**: current instant, in ms.
+ **proprioceptorFR**: proprioceptor firing rate, in Hz.
'''
self.spikesGenerator.atualizeGenerator(t, proprioceptorFR, self.GammaOrder)
if self.proprioceptorSpikeTrain and -1e-3 < (t - self.proprioceptorSpikeTrain[-1][0]) < 1e-3:
self.Delay.addSpinalSpike(t)
if self.compNumber:
self.atualizeCompartments(t)
self.atualizeDelay(t)
#@profile
def atualizeCompartments(self, t):
'''
Atualize all neural compartments.
- Inputs:
+ **t**: current instant, in ms.
'''
np.clip(runge_kutta(self.dVdt, t, self.v_mV, self.timeStep_ms, self.timeStepByTwo_ms, self.conf.timeStepBySix_ms), -30.0, 120.0, self.v_mV)
for i in xrange(self.somaIndex, self.compNumber):
if self.v_mV[i] > self.threshold_mV and t-self.tSpikes[i] > self.MNRefPer_ms:
self.addCompartmentSpike(t, i)
#@profile
def dVdt(self, t, V):
'''
Compute the potential derivative of all compartments of the motor unit.
- Inputs:
+ **t**: current instant, in ms.
+ **V**: Vector with the current potential value of all neural
compartments of the motor unit.
\f{equation}{
\frac{dV}{dt} = (I_{active} + GV+ I_{inj} + I_{eq})C_inv
}
where all the variables are vectors with the number of elements equal
to the number of compartments and \f$G\f$ is the conductance matrix built
in the compGCouplingMatrix function.
'''
for i in xrange(self.compNumber):
self.iIonic.itemset(i, self.compartment[i].computeCurrent(t, V.item(i)))
return (self.iIonic + self.G.dot(V) + self.iInjected + self.EqCurrent_nA) * self.capacitanceInv
#@profile
def addCompartmentSpike(self, t, comp):
'''
When the soma potential is above the threshold a spike is added tom the soma.
- Inputs:
+ **t**: current instant, in ms.
+ **comp**: integer with the compartment index.
'''
self.tSpikes[comp] = t
if comp == self.somaIndex:
self.somaSpikeTrain.append([t, int(self.index)])
self.transmitSpikes(t)
if comp == self.lastCompIndex:
self.lastCompSpikeTrain.append([t, int(self.index)])
self.Delay.addSpinalSpike(t)
for channel in self.compartment[comp].Channels:
for channelState in channel.condState: channelState.changeState(t)
def atualizeDelay(self, t):
'''
Atualize the terminal spike train, by considering the Delay of the nerve.
- Inputs:
+ **t**: current instant, in ms.
'''
if -1e-3 < (t - self.Delay.terminalSpikeTrain) < 1e-3:
self.terminalSpikeTrain.append([t, self.index])
self.transmitSpikes(t)
if self.stimulusCompartment == 'delay':
self.Delay.atualizeStimulus(t, self.nerveStimulus_mA[int(np.rint(t/self.conf.timeStep_ms))])
def transmitSpikes(self, t):
'''
- Inputs:
+ **t**: current instant, in ms.
'''
for i in xrange(len(self.indicesOfSynapsesOnTarget)):
self.transmitSpikesThroughSynapses[i].receiveSpike(t, self.indicesOfSynapsesOnTarget[i])
def createStimulus(self):
'''
'''
self.stimulusMeanFrequency_Hz = float(self.conf.parameterSet('stimFrequency_' + self.nerve, self.pool, 0))
self.stimulusPulseDuration_ms = float(self.conf.parameterSet('stimPulseDuration_' + self.nerve, self.pool, 0))
self.stimulusIntensity_mA = float(self.conf.parameterSet('stimIntensity_' + self.nerve, self.pool, 0))
self.stimulusStart_ms = float(self.conf.parameterSet('stimStart_' + self.nerve, self.pool, 0))
self.stimulusStop_ms = float(self.conf.parameterSet('stimStop_' + self.nerve, self.pool, 0))
self.stimulusModulationStart_ms = float(self.conf.parameterSet('stimModulationStart_' + self.nerve, self.pool, 0))
self.stimulusModulationStop_ms = float(self.conf.parameterSet('stimModulationStop_' + self.nerve, self.pool, 0))
## Vector with the nerve stimulus, in mA.
self.nerveStimulus_mA = np.zeros((int(np.rint(self.conf.simDuration_ms/self.conf.timeStep_ms)), 1), dtype = float)
startStep = int(np.rint(self.stimulusStart_ms / self.conf.timeStep_ms))
for i in xrange(len(self.nerveStimulus_mA)):
if (i * self.conf.timeStep_ms >= self.stimulusStart_ms and i * self.conf.timeStep_ms <= self.stimulusStop_ms):
if (i * self.conf.timeStep_ms > self.stimulusModulationStart_ms and i * self.conf.timeStep_ms < self.stimulusModulationStop_ms):
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz + self.axonStimModulation(i * self.conf.timeStep_ms)
else:
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz
if stimulusFrequency_Hz > 0:
stimulusPeriod_ms = 1000.0 / stimulusFrequency_Hz
numberOfSteps = int(np.rint(stimulusPeriod_ms / self.conf.timeStep_ms))
if ((i - startStep) % numberOfSteps == 0):
self.nerveStimulus_mA[i:int(np.rint(i + self.stimulusPulseDuration_ms / self.conf.timeStep_ms))] = self.stimulusIntensity_mA
def reset(self):
'''
'''
self.v_mV = np.zeros((self.compNumber), dtype = np.float64)
for i in xrange(len(self.compartment)):
self.v_mV[i] = self.compartment[i].EqPot_mV
self.Delay.reset()
self.tSpikes = np.zeros((self.compNumber), dtype = np.float64)
self.lastCompSpikeTrain = []
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
def __init__(self, conf, pool, index, kind):
'''
Constructor
- Inputs:
+ **conf**: Configuration object with the simulation parameters.
cyto + **pool**: string with Motor unit pool to which the motor
unit belongs.
+ **index**: integer corresponding to the motor unit order in
the pool, according to the Henneman's principle (size principle).
+ **kind**: string with the type of the motor unit. It can
be *S* (slow), *FR* (fast and resistant), and
*FF* (fast and fatigable).
'''
## Configuration object with the simulation parameters.
self.conf = conf
## String with the type of the motor unit. It can be
## *S* (slow), *FR* (fast and resistant) and
## *FF** (fast and fatigable).
self.kind = kind
# Neural compartments
## The instant of the last spike of the Motor unit
## at the Soma compartment.
self.tSomaSpike = float("-inf")
compartmentsList = ['dendrite', 'soma']
## Vector with the instants of spikes at the soma.
self.somaSpikeTrain = []
## Integer corresponding to the motor unit order in the pool, according to the Henneman's principle (size principle).
self.index = int(index)
## Vector of Compartment of the Motor Unit.
self.compartment = []
## Value of the membrane potential, in mV, that is considered a spike.
self.threshold_mV = conf.parameterSet('threshold', pool, index)
## Anatomical position of the neuron, in mm.
self.position_mm = conf.parameterSet('position', pool, index)
for i in compartmentsList: self.compartment.append(Compartment(i, conf, pool, index, self.kind))
## Number of compartments.
self.compNumber = len(self.compartment)
## Vector with membrane potential,in mV, of all compartments.
self.v_mV = np.zeros((self.compNumber), dtype = np.float64)
gCoupling_MS = np.zeros_like(self.v_mV, dtype = 'd')
gLeak = np.zeros_like(self.v_mV, dtype = 'd')
for i in self.compartment[0:-1]: gCoupling_MS[self.compartment.index(i)] = calcGCoupling(float(conf.parameterSet('cytR',pool, index)),
self.compartment[self.compartment.index(i)].length_mum,
self.compartment[self.compartment.index(i) + 1].length_mum,
self.compartment[self.compartment.index(i)].diameter_mum,
self.compartment[self.compartment.index(i) + 1].diameter_mum)
capacitance_nF = np.zeros_like(self.v_mV, dtype = 'd')
for i in self.compartment:
capacitance_nF[self.compartment.index(i)] = i.capacitance_nF
gLeak[self.compartment.index(i)] = i.gLeak
## Vector with the inverse of the capacitance of all compartments.
self.capacitanceInv = 1 / capacitance_nF
## Vector with current, in nA, of each compartment coming from other elements of the model. For example
## from ionic channels and synapses.
self.iIonic = np.full_like(self.v_mV, 0.0)
## Vector with the current, in nA, injected in each compartment.
self.iInjected = np.zeros_like(self.v_mV, dtype = 'd')
#self.iInjected = np.array([0, 10.0])
GC = compGCouplingMatrix(gCoupling_MS)
GL = -np.diag(gLeak)
## Matrix of the conductance of the motoneuron. Multiplied by the vector self.v_mV,
## results in the passive currents of each compartment.
self.G = np.float64(GC + GL)
## index of the soma compartment.
self.somaIndex = compartmentsList.index('soma')
## Refractory period, in ms, of the motoneuron.
self.MNRefPer_ms = float(conf.parameterSet('MNSomaRefPer', pool, index))
# delay
## String with type of the nerve. It can be PTN (posterior tibial nerve) or CPN
## (common peroneal nerve).
if (pool == 'SOL' or pool == 'MG' or pool == 'LG'):
self.nerve = 'PTN'
else:
self.nerve = 'CPN'
## AxonDelay object of the motor unit.
self.Delay = AxonDelay(conf, self.nerve, pool, index)
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
# contraction DataMUnumber_S = int(conf.parameterSet('MUnumber_S_' + pool, pool, 0))
activationModel = conf.parameterSet('activationModel', pool, 0)
## Contraction time of the twitch muscle unit, in ms.
self.TwitchTc_ms = conf.parameterSet('twitchTimePeak', pool, index)
## Amplitude of the muscle unit twitch, in N.
self.TwitchAmp_N = conf.parameterSet('twitchPeak', pool, index)
## Parameter of the saturation.
self.bSat = conf.parameterSet('bSat'+ activationModel,pool,index)
## Twitch- tetanus relationship
self.twTet = conf.parameterSet('twTet' + activationModel,pool,index)
## EMG data
## Build synapses
self.SynapsesOut = []
self.transmitSpikesThroughSynapses = []
self.indicesOfSynapsesOnTarget = []
class MotorUnit(object):
'''
Class that implements a motor unit model. Encompasses a motoneuron
and a muscle unit.
'''
def __init__(self, conf, pool, index, kind):
'''
Constructor
- Inputs:
+ **conf**: Configuration object with the simulation parameters.
cyto + **pool**: string with Motor unit pool to which the motor
unit belongs.
+ **index**: integer corresponding to the motor unit order in
the pool, according to the Henneman's principle (size principle).
+ **kind**: string with the type of the motor unit. It can
be *S* (slow), *FR* (fast and resistant), and
*FF* (fast and fatigable).
'''
## Configuration object with the simulation parameters.
self.conf = conf
## String with the type of the motor unit. It can be
## *S* (slow), *FR* (fast and resistant) and
## *FF** (fast and fatigable).
self.kind = kind
# Neural compartments
## The instant of the last spike of the Motor unit
## at the Soma compartment.
self.tSomaSpike = float("-inf")
compartmentsList = ['dendrite', 'soma']
## Vector with the instants of spikes at the soma.
self.somaSpikeTrain = []
## Integer corresponding to the motor unit order in the pool, according to the Henneman's principle (size principle).
self.index = int(index)
## Vector of Compartment of the Motor Unit.
self.compartment = []
## Value of the membrane potential, in mV, that is considered a spike.
self.threshold_mV = conf.parameterSet('threshold', pool, index)
## Anatomical position of the neuron, in mm.
self.position_mm = conf.parameterSet('position', pool, index)
for i in compartmentsList: self.compartment.append(Compartment(i, conf, pool, index, self.kind))
## Number of compartments.
self.compNumber = len(self.compartment)
## Vector with membrane potential,in mV, of all compartments.
self.v_mV = np.zeros((self.compNumber), dtype = np.float64)
gCoupling_MS = np.zeros_like(self.v_mV, dtype = 'd')
gLeak = np.zeros_like(self.v_mV, dtype = 'd')
for i in self.compartment[0:-1]: gCoupling_MS[self.compartment.index(i)] = calcGCoupling(float(conf.parameterSet('cytR',pool, index)),
self.compartment[self.compartment.index(i)].length_mum,
self.compartment[self.compartment.index(i) + 1].length_mum,
self.compartment[self.compartment.index(i)].diameter_mum,
self.compartment[self.compartment.index(i) + 1].diameter_mum)
capacitance_nF = np.zeros_like(self.v_mV, dtype = 'd')
for i in self.compartment:
capacitance_nF[self.compartment.index(i)] = i.capacitance_nF
gLeak[self.compartment.index(i)] = i.gLeak
## Vector with the inverse of the capacitance of all compartments.
self.capacitanceInv = 1 / capacitance_nF
## Vector with current, in nA, of each compartment coming from other elements of the model. For example
## from ionic channels and synapses.
self.iIonic = np.full_like(self.v_mV, 0.0)
## Vector with the current, in nA, injected in each compartment.
self.iInjected = np.zeros_like(self.v_mV, dtype = 'd')
#self.iInjected = np.array([0, 10.0])
GC = compGCouplingMatrix(gCoupling_MS)
GL = -np.diag(gLeak)
## Matrix of the conductance of the motoneuron. Multiplied by the vector self.v_mV,
## results in the passive currents of each compartment.
self.G = np.float64(GC + GL)
## index of the soma compartment.
self.somaIndex = compartmentsList.index('soma')
## Refractory period, in ms, of the motoneuron.
self.MNRefPer_ms = float(conf.parameterSet('MNSomaRefPer', pool, index))
# delay
## String with type of the nerve. It can be PTN (posterior tibial nerve) or CPN
## (common peroneal nerve).
if (pool == 'SOL' or pool == 'MG' or pool == 'LG'):
self.nerve = 'PTN'
else:
self.nerve = 'CPN'
## AxonDelay object of the motor unit.
self.Delay = AxonDelay(conf, self.nerve, pool, index)
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
# contraction DataMUnumber_S = int(conf.parameterSet('MUnumber_S_' + pool, pool, 0))
activationModel = conf.parameterSet('activationModel', pool, 0)
## Contraction time of the twitch muscle unit, in ms.
self.TwitchTc_ms = conf.parameterSet('twitchTimePeak', pool, index)
## Amplitude of the muscle unit twitch, in N.
self.TwitchAmp_N = conf.parameterSet('twitchPeak', pool, index)
## Parameter of the saturation.
self.bSat = conf.parameterSet('bSat'+ activationModel,pool,index)
## Twitch- tetanus relationship
self.twTet = conf.parameterSet('twTet' + activationModel,pool,index)
## EMG data
## Build synapses
self.SynapsesOut = []
self.transmitSpikesThroughSynapses = []
self.indicesOfSynapsesOnTarget = []
def atualizeMotorUnit(self, t):
'''
Atualize the dynamical and nondynamical (delay) parts of the motor unit.
- Inputs:
+ **t**: current instant, in ms.
'''
self.atualizeCompartments(t)
self.atualizeDelay(t)
def atualizeCompartments(self, t):
'''
Atualize all neural compartments.
- Inputs:
+ **t**: current instant, in ms.
'''
np.clip(runge_kutta(self.dVdt, t, self.v_mV, self.conf.timeStep_ms, self.conf.timeStepByTwo_ms, self.conf.timeStepBySix_ms), -16.0, 120.0, self.v_mV)
if (self.v_mV[self.somaIndex] > self.threshold_mV and t-self.tSomaSpike > self.MNRefPer_ms): self.addSomaSpike(t)
def dVdt(self, t, V):
'''
Compute the potential derivative of all compartments of the motor unit.
- Inputs:
+ **t**: current instant, in ms.
+ **V**: Vector with the current potential value of all neural
compartments of the motor unit.
\f{equation}{
\frac{dV}{dt} = (I_{active} + GV+ I_{inj})C_inv
}
where all the variables are vectors with the number of elements equal
to the number of compartments and \f$G\f$ is the conductance matrix built
in the compGCouplingMatrix function.
'''
for compartment in xrange(0, self.compNumber):
self.iIonic.itemset(compartment, self.compartment[compartment].computeCurrent(t, V.item(compartment)))
return (self.iIonic + np.dot(self.G, V) + self.iInjected) * self.capacitanceInv
def addSomaSpike(self, t):
'''
When the soma potential is above the threshold a spike is added tom the soma.
- Inputs:
+ **t**: current instant, in ms.
'''
self.tSomaSpike = t
self.somaSpikeTrain.append([t, int(self.index)])
self.Delay.addSpinalSpike(t)
self.transmitSpikes(t)
for channel in self.compartment[self.somaIndex].Channels:
for channelState in channel.condState: channelState.changeState(t)
def atualizeDelay(self, t):
'''
Atualize the terminal spike train, by considering the Delay of the nerve.
- Inputs:
+ **t**: current instant, in ms.
'''
if abs(t - self.Delay.terminalSpikeTrain) < 1e-3:
self.terminalSpikeTrain.append([t, self.index])
def transmitSpikes(self, t):
'''
- Inputs:
+ **t**: current instant, in ms.
'''
for i in xrange(len(self.indicesOfSynapsesOnTarget)):
self.transmitSpikesThroughSynapses[i].receiveSpike(t, self.indicesOfSynapsesOnTarget[i])
class MotorUnit(object):
'''
Class that implements a motor unit model. Encompasses a motoneuron
and a muscle unit.
'''
def __init__(self, conf, pool, index, kind, muscleThickness,
skinThickness):
'''
Constructor
- Inputs:
+ **conf**: Configuration object with the simulation parameters.
+ **pool**: string with Motor unit pool to which the motor
unit belongs.
+ **index**: integer corresponding to the motor unit order in
the pool, according to the Henneman's principle (size principle).
+ **kind**: string with the type of the motor unit. It can
be *S* (slow), *FR* (fast and resistant), and
*FF* (fast and fatigable).
'''
## Configuration object with the simulation parameters.
self.conf = conf
## String with the type of the motor unit. It can be
## *S* (slow), *FR* (fast and resistant) and
## *FF** (fast and fatigable).
self.kind = kind
self.pool = pool
# Neural compartments
## The instant of the last spike of the Motor unit
## at the Soma compartment.
self.tSomaSpike = float("-inf")
NumberOfAxonNodes = int(
conf.parameterSet('NumberAxonNodes', pool, index))
compartmentsList = ['dendrite', 'soma']
for i in xrange(0, NumberOfAxonNodes):
compartmentsList.append('internode')
compartmentsList.append('node')
## Integer corresponding to the motor unit order in the pool, according to the Henneman's principle (size principle).
self.index = int(index)
## Dictionary of Compartment of the Motor Unit.
self.compartment = dict()
## Value of the membrane potential, in mV, that is considered a spike.
self.threshold_mV = conf.parameterSet('threshold', pool, index)
## Anatomical position of the neuron, in mm.
self.position_mm = conf.parameterSet('position', pool, index)
# EMG data
self.MUSpatialDistribution = conf.parameterSet('MUSpatialDistribution',
pool, 0)
if self.MUSpatialDistribution == 'random':
radius = (muscleThickness / 2) * np.random.uniform(0.0, 1.0)
angle = 2.0 * math.pi * np.random.uniform(0.0, 1.0)
x = radius * math.sin(angle)
y = radius * math.cos(angle)
## Anatomical coordinate of the muscle unit in a muscle section, in (mm,mm).
self.muSectionPosition_mm = [x, y]
## Distance of the MU to the EMG elctrode, in mm.
self.distance_mm = math.sqrt((x + muscleThickness / 2.0 +
skinThickness)**2 + y**2)
## Attenuation of the MUAP amplitude, as measured in the electrode.
self.attenuationToSkin = math.exp(-self.distance_mm /
conf.EMGAttenuation_mm1)
## Widening of the MUAP duration, as measured in the electrode.
self.timeWidening = 1 + conf.EMGWidening_mm1 * self.distance_mm
## Type of the Hermitez-Rodiguez curve. It can be 1 or 2.
self.hrType = np.random.random_integers(1, 2)
## MUAP amplitude in mV.
self.ampEMG_mV = conf.parameterSet('EMGAmplitude', pool, index)
self.ampEMG_mV = self.ampEMG_mV * self.attenuationToSkin
## MUAP time constant, in ms.
self.timeCteEMG_ms = conf.parameterSet('EMGDuration', pool, index)
self.timeCteEMG_ms = self.timeCteEMG_ms * self.timeWidening
for i in xrange(len(compartmentsList)):
self.compartment[i] = Compartment(compartmentsList[i], conf, pool,
index, self.kind)
## Number of compartments.
self.compNumber = len(self.compartment)
## Vector with membrane potential,in mV, of all compartments.
self.v_mV = np.zeros((self.compNumber), dtype=np.float64)
## Vector with the last instant of spike of all compartments.
self.tSpikes = np.zeros((self.compNumber), dtype=np.float64)
gCoupling_muS = np.zeros_like(self.v_mV, dtype='d')
for i in xrange(len(self.compartment) - 1):
gCoupling_muS[i] = calcGCoupling(
float(conf.parameterSet('cytR', pool, index)),
self.compartment[i].length_mum,
self.compartment[i + 1].length_mum,
self.compartment[i].diameter_mum,
self.compartment[i + 1].diameter_mum)
gLeak = np.zeros_like(self.v_mV, dtype='d')
capacitance_nF = np.zeros_like(self.v_mV, dtype='d')
EqPot = np.zeros_like(self.v_mV, dtype='d')
IPump = np.zeros_like(self.v_mV, dtype='d')
compLength = np.zeros_like(self.v_mV, dtype='d')
for i in xrange(len(self.compartment)):
capacitance_nF[i] = self.compartment[i].capacitance_nF
gLeak[i] = self.compartment[i].gLeak_muS
EqPot[i] = self.compartment[i].EqPot_mV
IPump[i] = self.compartment[i].IPump_nA
compLength[i] = self.compartment[i].length_mum
self.v_mV[i] = self.compartment[i].EqPot_mV
## Vector with the inverse of the capacitance of all compartments.
self.capacitanceInv = 1.0 / capacitance_nF
## Vector with current, in nA, of each compartment coming from other elements of the model. For example
## from ionic channels and synapses.
self.iIonic = np.full_like(self.v_mV, 0.0)
## Vector with the current, in nA, injected in each compartment.
self.iInjected = np.zeros_like(self.v_mV, dtype='d')
#self.iInjected = np.array([0, 10.0])
GC = compGCouplingMatrix(gCoupling_muS)
GL = -np.diag(gLeak)
## Matrix of the conductance of the motoneuron. Multiplied by the vector self.v_mV,
## results in the passive currents of each compartment.
self.G = np.float64(GC + GL)
self.EqCurrent_nA = np.dot(-GL, EqPot) + IPump
## index of the soma compartment.
self.somaIndex = compartmentsList.index('soma')
## index of the last compartment.
self.lastCompIndex = self.compNumber - 1
## Refractory period, in ms, of the motoneuron.
self.MNRefPer_ms = float(conf.parameterSet('MNSomaRefPer', pool,
index))
# delay
## String with type of the nerve. It can be PTN (posterior tibial nerve) or CPN
## (common peroneal nerve).
if pool == 'SOL' or pool == 'MG' or pool == 'LG':
self.nerve = 'PTN'
elif pool == 'TA':
self.nerve = 'CPN'
## Distance, in m, of the stimulus position to the terminal.
self.stimulusPositiontoTerminal = float(
conf.parameterSet('stimDistToTerm_' + self.nerve, pool, index))
## AxonDelay object of the motor unit.
if NumberOfAxonNodes == 0:
dynamicNerveLength = 0
else:
dynamicNerveLength = np.sum(compLength[2:-1]) * 1e-6
self.nerveLength = float(
conf.parameterSet('nerveLength_' + self.nerve, pool, index))
delayLength = self.nerveLength - dynamicNerveLength
if self.stimulusPositiontoTerminal < delayLength:
self.Delay = AxonDelay(conf, self.nerve, pool, delayLength,
self.stimulusPositiontoTerminal, index)
self.stimulusCompartment = 'delay'
else:
self.Delay = AxonDelay(conf, self.nerve, pool, delayLength, -1,
index)
self.stimulusCompartment = -1
# Nerve stimulus function
self.stimulusMeanFrequency_Hz = float(
conf.parameterSet('stimFrequency_' + self.nerve, pool, 0))
self.stimulusPulseDuration_ms = float(
conf.parameterSet('stimPulseDuration_' + self.nerve, pool, 0))
self.stimulusIntensity_mA = float(
conf.parameterSet('stimIntensity_' + self.nerve, pool, 0))
self.stimulusStart_ms = float(
conf.parameterSet('stimStart_' + self.nerve, pool, 0))
self.stimulusStop_ms = float(
conf.parameterSet('stimStop_' + self.nerve, pool, 0))
self.stimulusModulationStart_ms = float(
conf.parameterSet('stimModulationStart_' + self.nerve, pool, 0))
self.stimulusModulationStop_ms = float(
conf.parameterSet('stimModulationStop_' + self.nerve, pool, 0))
exec 'def axonStimModulation(t): return ' + conf.parameterSet(
'stimModulation_' + self.nerve, pool, 0)
startStep = int(np.rint(self.stimulusStart_ms / self.conf.timeStep_ms))
self.axonStimModulation = axonStimModulation
## Vector with the nerve stimulus, in mA.
self.nerveStimulus_mA = np.zeros(
(int(np.rint(conf.simDuration_ms / conf.timeStep_ms)), 1),
dtype=float)
for i in xrange(len(self.nerveStimulus_mA)):
if (i * self.conf.timeStep_ms >= self.stimulusStart_ms
and i * self.conf.timeStep_ms <= self.stimulusStop_ms):
if (i * self.conf.timeStep_ms > self.stimulusModulationStart_ms
and i * self.conf.timeStep_ms <
self.stimulusModulationStop_ms):
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz + axonStimModulation(
i * self.conf.timeStep_ms)
else:
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz
if stimulusFrequency_Hz > 0:
stimulusPeriod_ms = 1000.0 / stimulusFrequency_Hz
numberOfSteps = int(
np.rint(stimulusPeriod_ms / self.conf.timeStep_ms))
if ((i - startStep) % numberOfSteps == 0):
self.nerveStimulus_mA[i:int(
np.rint(i + self.stimulusPulseDuration_ms /
self.conf.timeStep_ms)
)] = self.stimulusIntensity_mA
#
## Vector with the instants of spikes at the soma.
self.somaSpikeTrain = []
## Vector with the instants of spikes at the last compartment.
self.lastCompSpikeTrain = []
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
# contraction DataMUnumber_S = int(conf.parameterSet('MUnumber_S_' + pool, pool, 0))
activationModel = conf.parameterSet('activationModel', pool, 0)
## Contraction time of the twitch muscle unit, in ms.
self.TwitchTc_ms = conf.parameterSet('twitchTimePeak', pool, index)
## Amplitude of the muscle unit twitch, in N.
self.TwitchAmp_N = conf.parameterSet('twitchPeak', pool, index)
## Parameter of the saturation.
self.bSat = conf.parameterSet('bSat' + activationModel, pool, index)
## Twitch- tetanus relationship
self.twTet = conf.parameterSet('twTet' + activationModel, pool, index)
## EMG data
## Build synapses
self.SynapsesOut = []
self.transmitSpikesThroughSynapses = []
self.indicesOfSynapsesOnTarget = []
def atualizeMotorUnit(self, t):
'''
Atualize the dynamical and nondynamical (delay) parts of the motor unit.
- Inputs:
+ **t**: current instant, in ms.
'''
self.atualizeCompartments(t)
self.atualizeDelay(t)
#@profile
def atualizeCompartments(self, t):
'''
Atualize all neural compartments.
- Inputs:
+ **t**: current instant, in ms.
'''
np.clip(
runge_kutta(self.dVdt, t, self.v_mV, self.conf.timeStep_ms,
self.conf.timeStepByTwo_ms,
self.conf.timeStepBySix_ms), -30.0, 120.0, self.v_mV)
for i in xrange(self.somaIndex, self.compNumber):
if self.v_mV[i] > self.threshold_mV and t - self.tSpikes[
i] > self.MNRefPer_ms:
self.addCompartmentSpike(t, i)
#@profile
def dVdt(self, t, V):
'''
Compute the potential derivative of all compartments of the motor unit.
- Inputs:
+ **t**: current instant, in ms.
+ **V**: Vector with the current potential value of all neural
compartments of the motor unit.
\f{equation}{
\frac{dV}{dt} = (I_{active} + GV+ I_{inj} + I_{eq})C_inv
}
where all the variables are vectors with the number of elements equal
to the number of compartments and \f$G\f$ is the conductance matrix built
in the compGCouplingMatrix function.
'''
for i in xrange(self.compNumber):
self.iIonic.itemset(
i, self.compartment[i].computeCurrent(t, V.item(i)))
return (self.iIonic + self.G.dot(V) + self.iInjected +
self.EqCurrent_nA) * self.capacitanceInv
#@profile
def addCompartmentSpike(self, t, comp):
'''
When the soma potential is above the threshold a spike is added tom the soma.
- Inputs:
+ **t**: current instant, in ms.
+ **comp**: integer with the compartment index.
'''
self.tSpikes[comp] = t
if comp == self.somaIndex:
self.somaSpikeTrain.append([t, int(self.index)])
self.transmitSpikes(t)
if comp == self.lastCompIndex:
self.lastCompSpikeTrain.append([t, int(self.index)])
self.Delay.addSpinalSpike(t)
for channel in self.compartment[comp].Channels:
for channelState in channel.condState:
channelState.changeState(t)
def atualizeDelay(self, t):
'''
Atualize the terminal spike train, by considering the Delay of the nerve.
- Inputs:
+ **t**: current instant, in ms.
'''
if -1e-3 < (t - self.Delay.terminalSpikeTrain) < 1e-3:
self.terminalSpikeTrain.append([t, self.index])
# Check whether there is antidromic impulse reaching soma or RC
if self.Delay.indexAntidromicSpike < len(
self.Delay.antidromicSpikeTrain) and -1e-2 < (
t - self.Delay.antidromicSpikeTrain[
self.Delay.indexAntidromicSpike]) < 1e-2:
# Considers only MN-RC connections
self.transmitSpikes(t)
# Refractory period of MN soma
if t - self.tSpikes[self.somaIndex] > self.MNRefPer_ms:
self.tSpikes[self.somaIndex] = t
self.somaSpikeTrain.append([t, int(self.index)])
self.Delay.indexAntidromicSpike += 1
for channel in self.compartment[self.somaIndex].Channels:
for channelState in channel.condState:
channelState.changeState(t)
if self.stimulusCompartment == 'delay':
self.Delay.atualizeStimulus(
t,
self.nerveStimulus_mA[int(np.rint(t / self.conf.timeStep_ms))])
def transmitSpikes(self, t):
'''
- Inputs:
+ **t**: current instant, in ms.
'''
for i in xrange(len(self.indicesOfSynapsesOnTarget)):
self.transmitSpikesThroughSynapses[i].receiveSpike(
t, self.indicesOfSynapsesOnTarget[i])
def getEMG(self, t):
'''
'''
emg = 0
numberOfSpikesUntilt = []
ta = 0
if (len(self.terminalSpikeTrain) == 0):
emg = 0
else:
for spike in self.terminalSpikeTrain:
if spike[0] < t:
numberOfSpikesUntilt.append(spike[0])
for spikeInstant in numberOfSpikesUntilt:
ta = t - spikeInstant - 3 * self.timeCteEMG_ms
if (ta <= 6 * self.timeCteEMG_ms):
if (self.hrType == 1):
emg += 1.19 * self.ampEMG_mV * ta * math.exp(
-(ta / self.timeCteEMG_ms)**2) / self.timeCteEMG_ms
elif (self.hrType == 2):
emg += 0.69 * self.ampEMG_mV * (1 - 2 * (
(ta / self.timeCteEMG_ms)**2)) * math.exp(
-(ta / self.timeCteEMG_ms)**2)
return emg
def createStimulus(self):
'''
'''
self.stimulusMeanFrequency_Hz = float(
self.conf.parameterSet('stimFrequency_' + self.nerve, self.pool,
0))
self.stimulusPulseDuration_ms = float(
self.conf.parameterSet('stimPulseDuration_' + self.nerve,
self.pool, 0))
self.stimulusIntensity_mA = float(
self.conf.parameterSet('stimIntensity_' + self.nerve, self.pool,
0))
self.stimulusStart_ms = float(
self.conf.parameterSet('stimStart_' + self.nerve, self.pool, 0))
self.stimulusStop_ms = float(
self.conf.parameterSet('stimStop_' + self.nerve, self.pool, 0))
self.stimulusModulationStart_ms = float(
self.conf.parameterSet('stimModulationStart_' + self.nerve,
self.pool, 0))
self.stimulusModulationStop_ms = float(
self.conf.parameterSet('stimModulationStop_' + self.nerve,
self.pool, 0))
startStep = int(np.rint(self.stimulusStart_ms / self.conf.timeStep_ms))
for i in xrange(len(self.nerveStimulus_mA)):
if (i * self.conf.timeStep_ms >= self.stimulusStart_ms
and i * self.conf.timeStep_ms <= self.stimulusStop_ms):
if (i * self.conf.timeStep_ms > self.stimulusModulationStart_ms
and i * self.conf.timeStep_ms <
self.stimulusModulationStop_ms):
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz + self.axonStimModulation(
i * self.conf.timeStep_ms)
else:
stimulusFrequency_Hz = self.stimulusMeanFrequency_Hz
if stimulusFrequency_Hz > 0:
stimulusPeriod_ms = 1000.0 / stimulusFrequency_Hz
numberOfSteps = int(
np.rint(stimulusPeriod_ms / self.conf.timeStep_ms))
if ((i - startStep) % numberOfSteps == 0):
self.nerveStimulus_mA[
i:int(np.rint(i + 1.0 / self.conf.timeStep_ms)
)] = self.stimulusIntensity_mA
def reset(self):
'''
'''
self.tSomaSpike = float("-inf")
self.v_mV = np.zeros((self.compNumber), dtype=np.float64)
for i in xrange(len(self.compartment)):
self.v_mV[i] = self.compartment[i].EqPot_mV
self.compartment[i].reset()
self.Delay.reset()
self.tSpikes = np.zeros((self.compNumber), dtype=np.float64)
self.iIonic = np.full_like(self.v_mV, 0.0)
self.iInjected = np.zeros_like(self.v_mV, dtype='d')
self.somaSpikeTrain = []
## Vector with the instants of spikes at the last compartment.
self.lastCompSpikeTrain = []
## Vector with the instants of spikes at the terminal.
self.terminalSpikeTrain = []
