def evaluate_netparams(candidates, args):
fitnessCandidates = []
for icand, cand in enumerate(candidates):
# modify network params based on this candidate params (genes)
tut2.netParams.connParams['S->M']['probability'] = cand[0]
tut2.netParams.connParams['S->M']['weight'] = cand[1]
tut2.netParams.connParams['S->M']['delay'] = cand[2]
# create network
sim.createSimulate(netParams=tut2.netParams, simConfig=tut2.simConfig)
# calculate firing rate
numSpikes = float(len(sim.simData['spkt']))
numCells = float(len(sim.net.cells))
duration = tut2.simConfig.duration / 1000.0
netFiring = numSpikes / numCells / duration
# calculate fitness for this candidate
fitness = abs(targetFiring -
netFiring) # minimize absolute difference in firing rate
# add to list of fitness for each candidate
fitnessCandidates.append(fitness)
# print candidate parameters, firing rate, and fitness
print '\n CHILD/CANDIDATE %d: Network with prob:%.2f, weight:%.2f, delay:%.1f \n firing rate: %.1f, FITNESS = %.2f \n'\
%(icand, cand[0], cand[1], cand[2], netFiring, fitness)
return fitnessCandidates
def run_sim():
# Folder that contains x86_64 folder
NETPYNE_WORKDIR_PATH = "../../../"
neuron.load_mechanisms(NETPYNE_WORKDIR_PATH)
netParams = sim.loadNetParams("./netParams.json", None, False)
simConfig = sim.loadSimCfg("./simConfig.json", None, False)
sim.createSimulate(netParams, simConfig)
sim.saveData()
def run():
# Folder that contains x86_64 folder
NETPYNE_WORKDIR_PATH = "../../../"
neuron.load_mechanisms(NETPYNE_WORKDIR_PATH)
# read cfg and netParams from command line arguments if available; otherwise use default
simConfig, netParams = sim.readCmdLineArgs(simConfigDefault="cfg.py",
netParamsDefault="netParams.py")
# Create network and run simulation
sim.createSimulate(netParams=netParams, simConfig=simConfig)
sim.saveData()
def evaluate_netparams(candidates, args):
global fitnessCandidates, fitness
fitnessCandidates = []
for icand, cand in enumerate(candidates):
# modify network params based on this candidate params (genes)
netParams_SGGA_markov.SGcellRule['secs']['soma']['mechs']['na11a'][
'gbar'] = cand[0]
netParams_SGGA_markov.SGcellRule['secs']['soma']['mechs']['na12a'][
'gbar'] = cand[1]
netParams_SGGA_markov.SGcellRule['secs']['soma']['mechs']['na13a'][
'gbar'] = cand[2]
netParams_SGGA_markov.SGcellRule['secs']['soma']['mechs']['na16a'][
'gbar'] = cand[3]
netParams_SGGA_markov.SGcellRule['secs']['soma']['mechs']['KDRI'][
'gkbar'] = cand[2]
# create network
sim.createSimulate(netParams=netParams_SGGA_markov.netParams,
simConfig=cfg)
# calculate FIRING RATE for comparison
# numSpikes = float(len(sim.simData['spkt']))
# numCells = float(len(sim.net.cells))
# duration = cfg.duration/1000.0
# netFiring = numSpikes/numCells/duration
# # calculate fitness for this candidate
# fitness = abs(targetFiring - netFiring) # minimize absolute difference in firing rate
### this is the comparison of sum of current traces
# target membrane voltage trace from the previous data
# obs_data = open("model_output_ori_ina.json", "r") # CHECK for the REFERENCE!!!
# obs_data = json.load(obs_data)
# targetCurr_ = obs_data["simData"]["B_Na"]["cell_0"]
# # calculate MEMBRANE VOLTAGE at interesting point for comparison
# sum_curr = np.array([])
# for i in range(len(sim.simData["t"])):
# sum_curr = sim.simData["na1.1"]["cell_0"][i] + sim.simData["na1.2"]["cell_0"][i] + sim.simData["na1.6"]["cell_0"][i]
# diff_curr = abs(targetCurr_[i] - sum_curr)
# sum_curr = np.append(sum_curr, diff_curr)
# fitness = sum(sum_curr) / 600
# this is the comparison of membrane voltage traces
#target membrane voltage trace from the previous data
obs_data = open("model_output_ori_ina_10ms.json",
"r") # CHECK for the REFERENCE!!!
obs_data = open("./data/original/NaV_0.json", "r")
obs_data = json.load(obs_data)
targetMemb_ = obs_data["simData"]["V_soma"]["cell_0"]
# # calculate MEMBRANE VOLTAGE at interesting point for comparison
sum_vol = []
for i in range(len(sim.simData["t"])):
diff_vol_ = abs(targetMemb_[i] -
sim.simData["V_soma"]["cell_0"][i])
diff_vol = np.array(diff_vol_)
sum_vol.append(diff_vol)
# #sum_vol_ext = sum_vol[499:999] # extract values from 10ms to 20ms
fitness = sum(sum_vol) / 600
# add to list of fitness for each candidate
fitnessCandidates.append(fitness)
# print candidate parameters, firing rate, and fitness
#print('\n CHILD/CANDIDATE %d: Network with na11:%.2f, na12:%.2f, na16:%.2f \n FITNESS = %.2f \n'\
#%(icand, cand[0], cand[1], cand[2], netFiring, fitness))
# original print candidate parameters, firing rate, and fitness
print('\n CHILD/CANDIDATE %d: Network with na12a:%.2f, KDRI:%.2f \n firing rate: %.1f, FITNESS = %.2f \n'\
%(icand, cand[0], cand[1], fitness))
#print('\n CHILD/CANDIDATE %d: Network with na11a:%.2f, na12a:%.2f, na13a:%.1f, na16a:%.1f, KDRI:%.1f \n firing rate: %.1f, FITNESS = %.2f \n'\
#%(icand, cand[0], cand[1], cand[2], cand[3], cand[4], netFiring, fitness))
return fitnessCandidates
import neuron
from netpyne import sim
# Folder that contains x86_64 folder
NETPYNE_WORKDIR_PATH = '../../../'
neuron.load_mechanisms(NETPYNE_WORKDIR_PATH)
sim.load("./model_output.json")
sim.createSimulate()
sim.saveData()
'var': 'v'
}
} # Dict with traces to record
simConfig.recordStep = 0.01 #0.025 # Step size in ms to save data (eg. V traces, LFP, etc)
simConfig.filename = 'model_output' # Set file output name
simConfig.saveMat = True
# Save .mat file after simulation
simConfig.timestampFilename = True # Add time stamp to filename
#simConfig.analysis['plotRaster'] = True #{'orderInverse': True, 'saveFig': 'tut_import_raster.png'} # Plot a raster
#simConfig.analysis['plotTraces'] = {'include': [('PN_A', [4,5,6]),('PN_B', [4,5,6])], 'overlay':True} # Plot recorded traces for this list of cells
#simConfig.analysis['plotTraces'] = {'include': [77]} # Plot recorded traces for this list of cells
#simConfig.analysis['plot2Dnet'] = True # Plot 2D visualization of cell positions and connections
# Create network and run simulation
sim.createSimulate(netParams=netParams, simConfig=simConfig)
sim.saveData()
import pylab
pylab.show(
) # this line is only necessary in certain systems where figures appear empty
'''
# Methods to modify your network
modifyCells(params) # Modify the cells in your network
modifySynMechs(params) # Modify the Synapse Mechanisms
modifyConns(params) # Modify the connections of your network
modifyStims(params) # Modify the stimulations of you network
'''
'''
#### Simulation setup to run multiple runs at once
simConfig = specs.SimConfig() # Object of class SimConfig to store simulation configuration