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gui_tut3_ip3high.py
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from netpyne import specs
#------------------------------------------------------------------------------
#
# NETWORK PARAMETERS
#
#------------------------------------------------------------------------------
netParams = specs.NetParams() # object of class NetParams to store the network parameters
netParams.sizeX = 100 # x-dimension (horizontal length) size in um
netParams.sizeY = 500 # y-dimension (vertical height or cortical depth) size in um
netParams.sizeZ = 100 # z-dimension (horizontal length) size in um
netParams.propVelocity = 100.0 # propagation velocity (um/ms)
netParams.probLengthConst = 150.0 # length constant for conn probability (um)
#------------------------------------------------------------------------------
## Cell parameters
netParams.loadCellParams(label='E', fileName='cells/CSTR_cellParams.json')
netParams.importCellParams(label='I', fileName='cells/FScell.hoc', cellName='FScell')
#------------------------------------------------------------------------------
## Population parameters
netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 10, 'yRange': [50,150]}
netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 10, 'yRange': [50,150]}
netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 10, 'yRange': [150,300]}
netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 10, 'yRange': [150,300]}
netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 10, 'ynormRange': [0.6,1.0]}
netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 10, 'ynormRange': [0.6,1.0]}
#------------------------------------------------------------------------------
## Synaptic mechanism parameters
netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0} # NMDA synaptic mechanism
netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75} # GABA synaptic mechanism
#------------------------------------------------------------------------------
# Stimulation parameters
netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 20, 'noise': 0.3}
netParams.stimTargetParams['bkg->E'] = {'source': 'bkg', 'conds': {'cellType': ['E']}, 'weight': 0.02, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}
netParams.stimTargetParams['bkg->I'] = {'source': 'bkg', 'conds': {'cellType': ['I']}, 'weight': 0.004, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}
#------------------------------------------------------------------------------
# Cell connectivity rules
netParams.connParams['E->all'] = {
'preConds': {'cellType': 'E'}, 'postConds': {'y': [50,500]}, # E -> all (100-1000 um)
'probability': 0.1, # probability of connection
'weight': '0.04*post_ynorm', # synaptic weight
'delay': 'dist_3D/propVelocity', # transmission delay (ms)
'synMech': 'exc'} # synaptic mechanism
netParams.connParams['I->E'] = {
'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']}, # I -> E
'probability': '0.3*exp(-dist_3D/probLengthConst)', # probability of connection
'weight': 0.01, # synaptic weight
'delay': 'dist_3D/propVelocity', # transmission delay (ms)
'sec': ['soma','Bdend'],
'synMech': 'inh'} # synaptic mechanism
#------------------------------------------------------------------------------
## RxD params
### constants
## Change ip3_init from 0 to 0.1 to observe multiscale effect:
## netParams.rxdParams['constants']['ip3_init'] = 0.1
## high ip3 -> ER Ca released to Cyt -> kBK channels open -> less firing
constants = {'ip3_init': 0.1, # initial ip3 concentration
'caDiff': 0.08, # calcium diffusion coefficient
'ip3Diff': 1.41, # ip3 diffusion coefficient
'caci_init': 1e-5, # intracellular calcium initial concentration
'caco_init': 2.0, # extracellular calcium initial concentration
'gip3r': 12040 * 100, # ip3 receptors density
'gserca': 0.3913, # SERCA conductance
'gleak': 6.020, # ER leak channel conductance
'kserca': 0.1, # SERCA reaction constant
'kip3': 0.15, # ip3 reaction constant
'kact': 0.4, #
'ip3rtau': 2000, # ip3 receptors time constant
'fc': 0.8, # fraction of cytosol
'fe': 0.2, # fraction of ER
'margin': 20} # extracellular volume additional margin
netParams.rxdParams['constants'] = constants
### regions
regions = {}
regions['cyt'] = {'cells': 'all', 'secs': 'all', 'nrn_region': 'i', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fc'], 'surface_fraction': 1}}}
regions['er'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fe']}}}
regions['cyt_er_membrane'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}}
margin = 20 # extracellular volume additional margin
x, y, z = [0-margin, 100+margin], [-500-margin, 0+margin], [0-margin, 100+margin]
regions['ecs'] = {'extracellular': True, 'xlo': x[0], 'ylo': y[0], 'zlo': z[0], 'xhi': x[1], 'yhi': y[1], 'zhi': z[1], 'dx': 5, 'volume_fraction': 0.2, 'tortuosity': 1.6}
netParams.rxdParams['regions'] = regions
### species
species = {}
species['ca'] = {'regions': ['cyt', 'er', 'ecs'], 'd': constants['caDiff'], 'charge': 2,
'initial': 'caco_init if isinstance(node,rxd.node.NodeExtracellular) else (0.0017 - caci_init * fc) / fe if node.region == er else caci_init'}
species['ip3'] = {'regions': ['cyt'], 'd': constants['ip3Diff'], 'initial': constants['ip3_init']}
netParams.rxdParams['species'] = species
### states
netParams.rxdParams['states'] = {'ip3r_gate_state': {'regions': ['cyt_er_membrane'], 'initial': 0.8}}
### reactions
minf = 'ip3[cyt] * 1000. * ca[cyt] / (ip3[cyt] + kip3) / (1000. * ca[cyt] + kact)'
h_gate = 'ip3r_gate_state[cyt_er_membrane]'
kip3 = 'gip3r * (%s * %s) ** 3' % (minf, h_gate)
mcReactions = {}
mcReactions['serca'] = {'reactant': 'ca[cyt]', 'product': 'ca[er]', 'rate_f': 'gserca / ((kserca / (1000. * ca[cyt])) ** 2 + 1)', 'membrane': 'cyt_er_membrane', 'custom_dynamics': True}
mcReactions['leak'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': constants['gleak'], 'rate_b': constants['gleak'], 'membrane': 'cyt_er_membrane'}
mcReactions['ip3r'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': kip3, 'rate_b': kip3, 'membrane': 'cyt_er_membrane'}
netParams.rxdParams['multicompartmentReactions'] = mcReactions
### rates
netParams.rxdParams['rates'] = {'ip3rg': {'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - %s) / ip3rtau'%(h_gate)}}
#------------------------------------------------------------------------------
#
# SIMULATION CONFIGURATION
#
#------------------------------------------------------------------------------
# Run parameters
simConfig = specs.SimConfig() # object of class simConfig to store simulation configuration
simConfig.duration = 1.0*1e3 # Duration of the simulation, in ms
simConfig.hParams['v_init'] = -65 # set v_init to -65 mV
simConfig.dt = 0.1 # Internal integration timestep to use
simConfig.verbose = False # Show detailed messages
simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc)
simConfig.filename = 'rxd_net' # Set file output name
# Recording/plotting parameters
simConfig.recordTraces = {'V_soma':{'sec': 'soma','loc': 0.5,'var': 'v'},
'ik_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'ik'},
'cai_soma': {'sec': 'soma', 'loc':0.5, 'var': 'cai'},
'cao_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'cao'}}
simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(int(netParams.sizeY/3), int(netParams.sizeY), int(netParams.sizeY/3))]
simConfig.analysis['iplotTraces'] = {'include': [0]}
simConfig.analysis['iplotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig': True, 'figSize': (9,3)} # Plot a raster
simConfig.analysis['iplotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'saveFig': True}
simConfig.analysis['iplotRxDConcentration'] = {'speciesLabel': 'ca', 'regionLabel': 'ecs'}
# ------------------------------------------------------------
# Run sim
# ------------------------------------------------------------
if __name__ == '__main__':
netpyne_geppetto.netParams=netParams
netpyne_geppetto.simConfig=simConfig
#from netpyne import sim
#sim.createSimulateAnalyze(netParams, simConfig)