def __init__(self, params, Settings):
from utils.SignalAnalysis import Signal, Envelope
# List of Parameters that will be modified during evolution
self.evolKeys = Settings['evolKeys']
# Initialize Wilson-Cowan Model
self.simFreq = 500 # simulation frequency in seconds
self.WC = WCModel(
Cmat=Settings['Cmat'],
Dmat=Settings['Dmat']) # Initialize Wilson Cowan model
self.NumberStrRegions = self.WC.params['N']
self.WC.params[
'duration'] = Settings['Duration'] * 1000 # Duration in ms
self.WC.params['dt'] = 1000 * (1 / self.simFreq) # timestep in ms
# Runt WC simulations with parameter
self._runWC(params)
# Calculate Low-pass Envelope of Carrier Frequency
self.Limits = Settings['CarrierFreq']
ExcSignal = Signal(self.WC.outputs.exc, self.simFreq)
self.ExcEnv = ExcSignal.getLowPassEnvelope(Limits=self.Limits)
# Downsample Envelope
self.downNum = 300 # has to be the same as for MEG CCD
self.dExcEnv = Envelope(self.ExcEnv).downsampleSignal(self.downNum)
# Get Fit
empCCD = Settings['empCCD']
self.Fit = self.getFit(empCCD)
def test_compare_w_neurolib_native_model(self):
"""
Compare with neurolib's native Wilson-Cowan model.
"""
# run this model
wc_multi = self._create_node()
multi_result = wc_multi.run(
DURATION,
DT,
ZeroInput(wc_multi.num_noise_variables).as_array(DURATION, DT),
backend="numba")
# run neurolib's model
wc_neurolib = WCModel(seed=SEED)
wc_neurolib.params["duration"] = DURATION
wc_neurolib.params["dt"] = DT
# match initial state
wc_neurolib.params["exc_init"] = np.array([[wc_multi.initial_state[0]]
])
wc_neurolib.params["inh_init"] = np.array([[wc_multi.initial_state[1]]
])
wc_neurolib.run()
for (var_multi, var_neurolib) in NEUROLIB_VARIABLES_TO_TEST:
corr_mat = np.corrcoef(wc_neurolib[var_neurolib],
multi_result[var_multi].values.T)
self.assertTrue(np.greater(corr_mat, CORR_THRESHOLD).all())
def test_network(self):
logging.info("\t > WC: Testing brain network (chunkwise integration and BOLD simulation) ...")
start = time.time()
ds = Dataset("gw")
wc = WCModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
wc.params["signalV"] = 4.0
wc.params["duration"] = 10 * 1000
wc.params["sigma_ou"] = 0.1
wc.params["K_gl"] = 0.6
wc.params["x_ext_mean"] = 0.72
wc.run(chunkwise=True, bold=True, append_outputs=True)
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
def test_single_node(self):
logging.info("\t > WC: Testing single node ...")
start = time.time()
wc = WCModel()
wc.params["duration"] = 2.0 * 1000
wc.params["sigma_ou"] = 0.03
wc.run()
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
def test_compare_w_neurolib_native_model(self):
"""
Compare with neurolib's native Wilson-Cowan model.
"""
wc_multi = WilsonCowanNetwork(self.SC, self.DELAYS)
multi_result = wc_multi.run(DURATION,
DT,
ZeroInput(DURATION, DT).as_array(),
backend="numba")
# run neurolib's model
wc_neurolib = WCModel(Cmat=self.SC, Dmat=self.DELAYS, seed=SEED)
wc_neurolib.params["duration"] = DURATION
wc_neurolib.params["dt"] = DT
# there is no "global coupling" parameter in MultiModel
wc_neurolib.params["K_gl"] = 1.0
# delays <-> length matrix
wc_neurolib.params["signalV"] = 1.0
wc_neurolib.params["sigma_ou"] = 0.0
# match initial state
wc_neurolib.params["exc_init"] = wc_multi.initial_state[::2][:, np.
newaxis]
wc_neurolib.params["inh_init"] = wc_multi.initial_state[1::2][:, np.
newaxis]
wc_neurolib.run()
for (var_multi, var_neurolib) in NEUROLIB_VARIABLES_TO_TEST:
for node_idx in range(len(wc_multi)):
corr_mat = np.corrcoef(
wc_neurolib[var_neurolib][node_idx, :],
multi_result[var_multi].values.T[node_idx, :])
self.assertTrue(np.greater(corr_mat, CORR_THRESHOLD).all())
class evWC():
"""
Class to handle the model simulations and
"""
def __init__(self, params, Settings):
from utils.SignalAnalysis import Signal, Envelope
# List of Parameters that will be modified during evolution
self.evolKeys = Settings['evolKeys']
# Initialize Wilson-Cowan Model
self.simFreq = 500 # simulation frequency in seconds
self.WC = WCModel(
Cmat=Settings['Cmat'],
Dmat=Settings['Dmat']) # Initialize Wilson Cowan model
self.NumberStrRegions = self.WC.params['N']
self.WC.params[
'duration'] = Settings['Duration'] * 1000 # Duration in ms
self.WC.params['dt'] = 1000 * (1 / self.simFreq) # timestep in ms
# Runt WC simulations with parameter
self._runWC(params)
# Calculate Low-pass Envelope of Carrier Frequency
self.Limits = Settings['CarrierFreq']
ExcSignal = Signal(self.WC.outputs.exc, self.simFreq)
self.ExcEnv = ExcSignal.getLowPassEnvelope(Limits=self.Limits)
# Downsample Envelope
self.downNum = 300 # has to be the same as for MEG CCD
self.dExcEnv = Envelope(self.ExcEnv).downsampleSignal(self.downNum)
# Get Fit
empCCD = Settings['empCCD']
self.Fit = self.getFit(empCCD)
def _runWC(self, params):
"""
:param params: list of parameters that are passed into the WC-model
:return: Envelopes of the excitatory and inhibitory spiking series
"""
print("Calculating Wilson-Cowan simulation.")
for idx, key in enumerate(self.evolKeys):
self.WC.params[key] = params[idx]
self.WC.params['exc_init'] = 0.05 * np.random.uniform(
0, 1, (self.NumberStrRegions, 1)) # setting random initial values
self.WC.params['inh_init'] = 0.05 * np.random.uniform(
0, 1, (self.NumberStrRegions, 1))
self.WC.run(chunkwise=True, append=True)
def getFit(self, empData, Type='CCD'):
"""Calculate the fit between empirical and WC-model with params as parameter.
The fit is defined as the KS Distance between the Coherence Connectivity Dynamics of empirical and
simulated data.
:param params: list of parameter that are passed into the WC-model
:return: Fit
"""
from utils.SignalAnalysis import Envelope
if Type == 'CCD':
self.simCCD = Envelope(self.dExcEnv).getCCD()
self.empCCD = empData
fit = self._getKSD(self.empCCD, self.simCCD)
if Type == 'FC':
self.simFC = Envelope(self.ExcEnv).getFC()
self.empFC = empData
fit = self._getKSD(self.empFC, self.simFC)
pass
print("Fit: ", fit)
return fit
def _getKSD(self, empMat, simMat):
"""
Returns the Kolmogorov-Smirnov distance which ranges from 0-1
:param simCCD: numpy ndarray containing the simulated CCD
:return: KS distance between simulated and empirical data
"""
from scipy.stats import ks_2samp
if empMat.shape != simMat.shape:
raise ValueError("Input matrices must have the same shape.")
rows = empMat.shape[-1]
idx = np.triu_indices(rows, k=1)
empValues = empMat[idx]
simValues = simMat[idx]
# Bin values
bins = np.arange(0, 1, 0.01)
simIdx = np.digitize(simValues, bins)
empIdx = np.digitize(empValues, bins)
binnedSim = bins[simIdx - 1]
binnedEmp = bins[empIdx - 1]
KSD = ks_2samp(
binnedEmp,
binnedSim)[0] # Omits the p-value and outputs the KSD distance
return KSD