'amplitude': 'inh_input', 'delay': 'input_delay', 'duration': 'input_duration' }) net.input_sources.append(input_source_i) net.inputs.append( Input(id='Inh_stim', input_source=input_source_i.id, population=inh_pop.id, percentage=100)) # Save to JSON format net.id = 'WC' new_file = net.to_json_file('WC.json') sim = Simulation(id='SimWC', duration='100', dt='0.005', network=new_file, recordRates={'all': '*'}, plots2D={ 'E-I': { 'x_axis': 'Excitatory/0/Exc/r', 'y_axis': 'Inhibitory/0/Inh/r' } }) sim.to_json_file('SimWC.nmllite.json') check_to_generate_or_run(sys.argv, sim)
def generate(): dt = 0.05 simtime = 100 ################################################################################ ### Build new network net = Network(id="FN") net.notes = "FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS" net.parameters = { "initial_w": 0.0, "initial_v": -1, "a_v": -0.3333333333333333, "b_v": 0.0, "c_v": 1.0, "d_v": 1, "e_v": -1.0, "f_v": 1.0, "time_constant_v": 1.0, "a_w": 1.0, "b_w": -0.8, "c_w": 0.7, "time_constant_w": 12.5, "threshold": -1.0, "mode": 1.0, "uncorrelated_activity": 0.0, "Iext": 0, } cellInput = Cell(id="fn", lems_source_file="FN_Definitions.xml", parameters={}) for p in net.parameters: cellInput.parameters[p] = p net.cells.append(cellInput) r1 = RectangularRegion(id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) pop = Population( id="FNpop", size="1", component=cellInput.id, properties={ "color": "0.2 0.2 0.2", "radius": 3 }, random_layout=RandomLayout(region=r1.id), ) net.populations.append(pop) new_file = net.to_json_file("%s.json" % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation( id="Sim%s" % net.id, network=new_file, duration=simtime, dt=dt, seed=123, recordVariables={ "V": { "all": "*" }, "W": { "all": "*" } }, plots2D={ "VW": { "x_axis": "%s/0/fn/V" % pop.id, "y_axis": "%s/0/fn/W" % pop.id } }, ) sim.to_json_file() return sim, net
def generate(): dt = 0.025 simtime = 500 ################################################################################ ### Build new network net = Network(id='SpikingEI') net.notes = 'SpikingEI' net.parameters = { 'order': 5, 'wee': 8, 'wei': 12, 'wie': -12, 'wii': -3, 'w_scale': 0.001, 'in_rate': 400, 'epsilon': 0.5, 'tauMem': 20.0, 'tauSyn': 0.1, 'tauRef': 2.0 } cell = Cell(id='ifcell', pynn_cell='IF_curr_alpha') cell.parameters = { 'tau_m': 'tauMem', 'tau_refrac': 'tauRef', 'v_rest': -70, 'v_reset': -70, 'v_thresh': -50, 'cm': 0.001, "i_offset": 0 } #cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml') net.cells.append(cell) poisson_input = Cell(id='poisson_input', pynn_cell='SpikeSourcePoisson') poisson_input.parameters = {'rate': 'in_rate', 'start': 0, 'duration': 1e9} net.cells.append(poisson_input) r1 = RectangularRegion(id='region1', x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) pE = Population(id='Excitatory', size='4*order', component=cell.id, properties={ 'color': '.9 0 0', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) pEpoisson = Population(id='expoisson', size='4*order', component=poisson_input.id, properties={ 'color': '0.9 0.7 0.7', 'radius': 3 }, random_layout=RandomLayout(region=r1.id)) pI = Population(id='Inhibitory', size='1*order', component=cell.id, properties={ 'color': '0 0 .9', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) pIpoisson = Population(id='inpoisson', size='1*order', component=poisson_input.id, properties={ 'color': '0.7 0.7 0.9', 'radius': 3 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pE) net.populations.append(pEpoisson) net.populations.append(pI) net.populations.append(pIpoisson) net.synapses.append( Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_alpha', parameters={'tau_syn': 0.1})) net.synapses.append( Synapse(id='gaba', pynn_receptor_type='inhibitory', pynn_synapse_type='curr_alpha', parameters={'tau_syn': 0.1})) delay_ext = dt net.projections.append( Projection(id='projEinput', presynaptic=pEpoisson.id, postsynaptic=pE.id, synapse='ampa', delay=delay_ext, weight=0.01, one_to_one_connector=OneToOneConnector())) net.projections.append( Projection(id='projIinput', presynaptic=pIpoisson.id, postsynaptic=pI.id, synapse='ampa', delay=delay_ext, weight=0.01, one_to_one_connector=OneToOneConnector())) net.projections.append( Projection( id='projEE', presynaptic=pE.id, postsynaptic=pE.id, synapse='ampa', delay=delay_ext, weight='wee * w_scale', random_connectivity=RandomConnectivity(probability='epsilon'))) net.projections.append( Projection( id='projEI', presynaptic=pE.id, postsynaptic=pI.id, synapse='ampa', delay=delay_ext, weight='wei * w_scale', random_connectivity=RandomConnectivity(probability='epsilon'))) net.projections.append( Projection( id='projIE', presynaptic=pI.id, postsynaptic=pE.id, synapse='gaba', delay=delay_ext, weight='wie * w_scale', random_connectivity=RandomConnectivity(probability='epsilon'))) net.projections.append( Projection( id='projII', presynaptic=pI.id, postsynaptic=pI.id, synapse='gaba', delay=delay_ext, weight='wii * w_scale', random_connectivity=RandomConnectivity(probability='epsilon'))) #print(net) #print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='SimSpiking', network=new_file, duration=simtime, dt=dt, seed=123, recordTraces={ pE.id: '*', pI.id: '*' }, recordSpikes={'all': '*'}) sim.to_json_file() return sim, net
def generate(): dt = 0.025 simtime = 1000 ################################################################################ ### Build new network net = Network(id='McCPNet') net.notes = 'Example of simplified McCulloch-Pitts based Network' net.parameters = {'amp': 1.5, 'scale': 3} cell = Cell(id='mccp0', lems_source_file='McCPTest.xml') net.cells.append(cell) silentDL = Synapse(id='silentSyn_proj0', lems_source_file='McCPTest.xml') net.synapses.append(silentDL) rsDL = Synapse(id='rsDL', lems_source_file='McCPTest.xml') net.synapses.append(rsDL) r1 = RectangularRegion(id='region1', x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) p0 = Population(id='McCPpop0', size='1*scale', component=cell.id, properties={ 'color': '.9 0.9 0', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(p0) p1 = Population(id='McCPpop1', size='1*scale', component=cell.id, properties={ 'color': '.9 0 0.9', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(p1) net.projections.append( Projection(id='proj0', presynaptic=p0.id, postsynaptic=p1.id, synapse=rsDL.id, pre_synapse=silentDL.id, type='continuousProjection', weight='random()', random_connectivity=RandomConnectivity(probability=0.6))) ''' net.synapses.append(Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_alpha', parameters={'tau_syn':0.1})) net.projections.append(Projection(id='proj1', presynaptic=pEpoisson.id, postsynaptic=pLNP.id, synapse='ampa', delay=0, weight='in_weight', random_connectivity=RandomConnectivity(probability=0.7)))''' input_source0 = InputSource(id='sg0', neuroml2_source_file='inputs.nml') net.input_sources.append(input_source0) input_source1 = InputSource(id='sg1', neuroml2_source_file='inputs.nml') net.input_sources.append(input_source1) for pop in [p0.id]: net.inputs.append( Input(id='stim0_%s' % pop, input_source=input_source0.id, population=pop, percentage=60)) net.inputs.append( Input(id='stim1_%s' % pop, input_source=input_source1.id, population=pop, percentage=60)) #print(net) #print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='Sim%s' % net.id, network=new_file, duration=simtime, dt=dt, seed=123, recordVariables={ 'R': { 'all': '*' }, 'ISyn': { 'all': '*' } }) sim.to_json_file() return sim, net
def generate(): dt = 0.025 simtime = 1000 ################################################################################ ### Build new network net = Network(id='Example7_Brunel2000') net.notes = 'Example 7: based on network of Brunel 2000' net.parameters = { 'g': 4, 'eta': 1, 'order': 5, 'epsilon': 0.1, 'J': 0.1, 'delay': 1.5, 'tauMem': 20.0, 'tauSyn': 0.1, 'tauRef': 2.0, 'U0': 0.0, 'theta': 20.0} cell = Cell(id='ifcell', pynn_cell='IF_curr_alpha') cell.parameters = { 'tau_m': 'tauMem', 'tau_refrac': 'tauRef', 'v_rest': 'U0', 'v_reset': 'U0', 'v_thresh': 'theta', 'cm': 0.001, "i_offset": 0} #cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml') net.cells.append(cell) poisson_input = Cell(id='poisson_input', pynn_cell='SpikeSourcePoisson') poisson_input.parameters = { 'rate': '1000 * (eta*theta/(J*4*order*epsilon*tauMem)) * (4*order*epsilon)', 'start': 0, 'duration': 1e9} net.cells.append(poisson_input) r1 = RectangularRegion(id='region1', x=0,y=0,z=0,width=1000,height=100,depth=1000) net.regions.append(r1) pE = Population(id='Epop', size='4*order', component=cell.id, properties={'color':'.9 0 0', 'radius':5}, random_layout = RandomLayout(region=r1.id)) pEpoisson = Population(id='expoisson', size='4*order', component=poisson_input.id, properties={'color':'0.9 0.7 0.7', 'radius':3}, random_layout = RandomLayout(region=r1.id)) pI = Population(id='Ipop', size='1*order', component=cell.id, properties={'color':'0 0 .9', 'radius':5}, random_layout = RandomLayout(region=r1.id)) pIpoisson = Population(id='inpoisson', size='1*order', component=poisson_input.id, properties={'color':'0.7 0.7 0.9', 'radius':3}, random_layout = RandomLayout(region=r1.id)) net.populations.append(pE) net.populations.append(pEpoisson) net.populations.append(pI) net.populations.append(pIpoisson) net.synapses.append(Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_alpha', parameters={'tau_syn':0.1})) net.synapses.append(Synapse(id='gaba', pynn_receptor_type='inhibitory', pynn_synapse_type='curr_alpha', parameters={'tau_syn':0.1})) delay_ext = dt downscale = 1 J_eff = 'J*%s'%(downscale) # synaptic weights, scaled for alpha functions, such that # for constant membrane potential, charge J would be deposited fudge = 0.00041363506632638 # ensures dV = J at V=0 JE = '((%s)/tauSyn)*%s'%(J_eff,fudge) JI = '-1*g*%s'%(JE) net.projections.append(Projection(id='projEinput', presynaptic=pEpoisson.id, postsynaptic=pE.id, synapse='ampa', delay=delay_ext, weight=JE, one_to_one_connector=OneToOneConnector())) net.projections.append(Projection(id='projIinput', presynaptic=pIpoisson.id, postsynaptic=pI.id, synapse='ampa', delay=delay_ext, weight=JE, one_to_one_connector=OneToOneConnector())) net.projections.append(Projection(id='projEE', presynaptic=pE.id, postsynaptic=pE.id, synapse='ampa', delay='delay', weight=JE, random_connectivity=RandomConnectivity(probability='epsilon'))) net.projections.append(Projection(id='projEI', presynaptic=pE.id, postsynaptic=pI.id, synapse='ampa', delay='delay', weight=JE, random_connectivity=RandomConnectivity(probability='epsilon'))) net.projections.append(Projection(id='projIE', presynaptic=pI.id, postsynaptic=pE.id, synapse='gaba', delay='delay', weight=JI, random_connectivity=RandomConnectivity(probability='epsilon'))) net.projections.append(Projection(id='projII', presynaptic=pI.id, postsynaptic=pI.id, synapse='gaba', delay='delay', weight=JI, random_connectivity=RandomConnectivity(probability='epsilon'))) #print(net) #print(net.to_json()) new_file = net.to_json_file('%s.json'%net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='SimExample7', network=new_file, duration=simtime, dt=dt, seed= 123, recordTraces={pE.id:[0,1],pI.id:[0,1]}, recordSpikes={pE.id:'*', pI.id:'*',pEpoisson.id:[0,1,2,3,4],pIpoisson.id:[0,1,2,3,4]}) sim.to_json_file() return sim, net
def generate(ref, np2=0, np5=0, nb2=0, nb5=0, recordTraces='*'): ################################################################################ ### Build new network net = Network(id=ref) net.notes = 'Example: %s...' % ref net.seed = 7890 net.temperature = 32 net.parameters = { 'np2': np2, 'np5': np5, 'nb2': nb2, 'nb5': nb5, 'offset_curr_l2p': -0.05, 'weight_bkg_l2p': 0.01, 'weight_bkg_l5p': 0.01 } l2p_cell = Cell(id='CELL_HH_reduced_L2Pyr', neuroml2_source_file='../CELL_HH_reduced_L2Pyr.cell.nml') net.cells.append(l2p_cell) l5p_cell = Cell(id='CELL_HH_reduced_L5Pyr', neuroml2_source_file='../CELL_HH_reduced_L5Pyr.cell.nml') net.cells.append(l5p_cell) l2b_cell = Cell(id='CELL_HH_simple_L2Basket', neuroml2_source_file='../CELL_HH_simple_L2Basket.cell.nml') net.cells.append(l2b_cell) l5b_cell = Cell(id='CELL_HH_simple_L5Basket', neuroml2_source_file='../CELL_HH_simple_L5Basket.cell.nml') net.cells.append(l5b_cell) input_source_poisson100 = InputSource(id='poissonFiringSyn100Hz', neuroml2_source_file='../inputs.nml') net.input_sources.append(input_source_poisson100) input_offset_curr_l2p = InputSource(id='input_offset_curr_l2p', pynn_input='DCSource', parameters={ 'amplitude': 'offset_curr_l2p', 'start': 0, 'stop': 1e9 }) net.input_sources.append(input_offset_curr_l2p) l2 = RectangularRegion(id='L2', x=0, y=1000, z=0, width=1000, height=10, depth=1000) net.regions.append(l2) l5 = RectangularRegion(id='L5', x=0, y=0, z=0, width=1000, height=10, depth=1000) net.regions.append(l5) #https://github.com/OpenSourceBrain/OpenCortex import opencortex.utils.color as occ pop_l2p = Population(id='pop_l2p', size='np2', component=l2p_cell.id, properties={'color': occ.L23_PRINCIPAL_CELL}, random_layout=RandomLayout(region=l2.id)) net.populations.append(pop_l2p) pop_l5p = Population(id='pop_l5p', size='np5', component=l5p_cell.id, properties={'color': occ.L5_PRINCIPAL_CELL}, random_layout=RandomLayout(region=l5.id)) net.populations.append(pop_l5p) pop_l2b = Population(id='pop_l2b', size='nb2', component=l2b_cell.id, properties={'color': occ.L23_INTERNEURON}, random_layout=RandomLayout(region=l2.id)) net.populations.append(pop_l2b) pop_l5b = Population(id='pop_l5b', size='nb5', component=l5b_cell.id, properties={'color': occ.L5_INTERNEURON}, random_layout=RandomLayout(region=l5.id)) net.populations.append(pop_l5b) # L2 -> L2 _add_projection(pop_l2p, pop_l2b, 'AMPA', delay=0, weight=0.001, probability=0.8, net=net) _add_projection(pop_l2b, pop_l2p, 'L2Pyr_GABAA', delay=0, weight=0.001, probability=0.8, net=net) _add_projection(pop_l2b, pop_l2p, 'L2Pyr_GABAB', delay=0, weight=0.001, probability=0.8, net=net) # L2 -> L5 _add_projection(pop_l2p, pop_l5p, 'L5Pyr_AMPA', delay=0, weight=0.001, probability=0.8, net=net) _add_projection(pop_l2p, pop_l5b, 'AMPA', delay=0, weight=0.001, probability=0.8, net=net) _add_projection(pop_l2b, pop_l5p, 'L5Pyr_GABAA', delay=0, weight=0.001, probability=0.8, net=net) # L5 -> L5 _add_projection(pop_l5p, pop_l5b, 'AMPA', delay=0, weight=0.001, probability=0.8, net=net) _add_projection(pop_l5b, pop_l5p, 'L5Pyr_GABAA', delay=0, weight=0.001, probability=0.8, net=net) _add_projection(pop_l5b, pop_l5p, 'L5Pyr_GABAB', delay=0, weight=0.001, probability=0.8, net=net) net.inputs.append( Input(id='stim_%s' % pop_l2p.id, input_source=input_source_poisson100.id, population=pop_l2p.id, percentage=100, weight='weight_bkg_l2p')) net.inputs.append( Input(id='stim_%s' % pop_l5p.id, input_source=input_source_poisson100.id, population=pop_l5p.id, percentage=100, weight='weight_bkg_l5p')) print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='Sim%s' % net.id, network=new_file, duration='500', seed='1111', dt='0.025', recordTraces={'all': recordTraces}, recordSpikes={'all': '*'}) sim.to_json_file() print(sim.to_json()) return sim, net
duration='1000', dt='0.1', network=new_file, recordVariables={ 'r': { 'all': '*' }, 'e': { 'all': '*' }, 'f': { 'all': '*' }, 'iTotal': { 'all': '*' }, 'nu': { 'all': '*' }, 'internalNoise': { 'all': '*' }, 'S': { 'all': '*' } }) sim.to_json_file('SimDemirtas_network.nmllite.json') check_to_generate_or_run(sys.argv, sim)
def generate(): dt = 0.025 simtime = 1000 ################################################################################ ### Build new network net = Network(id='ExampleIF') net.notes = 'Example with IF' net.parameters = { 'tauMem': 20.0, 'tauSyn': 0.1, 'tauRef': 2, 'V0': -70, 'theta': -50.0, 'scale': 1, 'in_weight': 0.01, 'in_rate': 50 } ifcell = Cell(id='ifcell', pynn_cell='IF_curr_alpha') ifcell.parameters = { 'tau_m': 'tauMem', 'tau_refrac': 'tauRef', 'v_rest': 'V0', 'v_reset': 'V0', 'v_thresh': 'theta', 'cm': 0.001, "i_offset": 0 } net.cells.append(ifcell) poisson_input = Cell(id='poisson_input', pynn_cell='SpikeSourcePoisson') poisson_input.parameters = {'rate': 'in_rate', 'start': 0, 'duration': 1e9} net.cells.append(poisson_input) r1 = RectangularRegion(id='region1', x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) pIF = Population(id='IFpop', size='1*scale', component=ifcell.id, properties={ 'color': '.9 0 0', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pIF) pLNP = Population(id='LNPpop', size='1*scale', component=ifcell.id, properties={ 'color': '.9 0.9 0', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pLNP) pEpoisson = Population(id='expoisson', size='10', component=poisson_input.id, properties={ 'color': '0.9 0.7 0.7', 'radius': 3 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pEpoisson) net.synapses.append( Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_alpha', parameters={'tau_syn': 0.1})) net.projections.append( Projection(id='proj0', presynaptic=pEpoisson.id, postsynaptic=pIF.id, synapse='ampa', delay=0, weight='in_weight', random_connectivity=RandomConnectivity(probability=0.7))) net.projections.append( Projection(id='proj1', presynaptic=pEpoisson.id, postsynaptic=pLNP.id, synapse='ampa', delay=0, weight='in_weight', random_connectivity=RandomConnectivity(probability=0.7))) #print(net) #print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='SimExampleIF', network=new_file, duration=simtime, dt=dt, seed=123, recordTraces={pIF.id: '*'}, recordSpikes={'all': '*'}) sim.to_json_file() return sim, net
def generate(): dt = 0.05 simtime = 100 ################################################################################ ### Build new network net = Network(id='FN') net.notes = 'Example of simplified network' net.parameters = { 'initial_w': 0.0, 'initial_v': -1, 'a_v': -0.3333333333333333, 'b_v': 0.0, 'c_v': 1.0, 'd_v': 1, 'e_v': -1.0, 'f_v': 1.0, 'time_constant_v': 1.0, 'a_w': 1.0, 'b_w': -0.8, 'c_w': 0.7, 'time_constant_w': 12.5, 'threshold': -1.0, 'mode': 1.0, 'uncorrelated_activity': 0.0, 'Iext': 0 } cellInput = Cell(id='fn', lems_source_file='FN_Definitions.xml', parameters={}) for p in net.parameters: cellInput.parameters[p]=p net.cells.append(cellInput) r1 = RectangularRegion(id='region1', x=0,y=0,z=0,width=1000,height=100,depth=1000) net.regions.append(r1) pop = Population(id='FNpop', size='1', component=cellInput.id, properties={'color':'0.2 0.2 0.2', 'radius':3}, random_layout = RandomLayout(region=r1.id)) net.populations.append(pop) new_file = net.to_json_file('%s.json'%net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='Sim%s'%net.id, network=new_file, duration=simtime, dt=dt, seed= 123, recordVariables={'V':{'all':'*'},'W':{'all':'*'}}, plots2D={'VW':{'x_axis':'%s/0/fn/V'%pop.id, 'y_axis':'%s/0/fn/W'%pop.id}}) sim.to_json_file() return sim, net
def generate(): ################################################################################ ### Build new network net = Network(id='RunStims') net.notes = 'Example with spike producers' net.parameters = { 'rate': 50, 'rateHz': '50Hz', 'periodms': '20ms'} ssp = Cell(id='ssp', pynn_cell='SpikeSourcePoisson') ssp.parameters = { 'rate': 'rate', 'start': 0, 'duration': 1e9} net.cells.append(ssp) sspPop = Population(id='sspPop', size=1, component=ssp.id, properties={'color':'.5 0 0'}) net.populations.append(sspPop) sg = Cell(id='sg', neuroml2_cell='SpikeGenerator') sg.parameters = { 'period': 'periodms'} net.cells.append(sg) sgPop = Population(id='sgPop', size=1, component=sg.id, properties={'color':'.5 0 0'}) net.populations.append(sgPop) sgp = Cell(id='sgp', neuroml2_cell='spikeGeneratorPoisson') sgp.parameters = { 'average_rate': 'rateHz'} net.cells.append(sgp) sgpPop = Population(id='sgpPop', size=1, component=sgp.id, properties={'color':'.5 0 0'}) net.populations.append(sgpPop) net.synapses.append(Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_alpha', parameters={'tau_syn':0.1})) net.synapses.append(Synapse(id='gaba', pynn_receptor_type='inhibitory', pynn_synapse_type='curr_alpha', parameters={'tau_syn':0.1})) #print(net) #print(net.to_json()) new_file = net.to_json_file('%s.json'%net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='SimTest', network=new_file, duration='10000', dt='0.025', seed= 123, recordTraces={'xxx':'*'}, recordSpikes={'all':'*'}) sim.to_json_file() return sim, net
def generate(ref='Example6_PyNN', add_inputs=True): ################################################################################ ### Build new network net = Network(id=ref, notes='Another network for PyNN - work in progress...') net.parameters = { 'N_scaling': 0.005, 'layer_height': 400, 'width': 100, 'depth': 100, 'input_weight': 0.1 } cell = Cell(id='CorticalCell', pynn_cell='IF_curr_exp') cell.parameters = { 'cm': 0.25, # nF 'i_offset': 0.0, # nA 'tau_m': 10.0, # ms 'tau_refrac': 2.0, # ms 'v_reset': -65.0, # mV 'v_rest': -65.0, # mV 'v_thresh': -50.0 # mV } net.cells.append(cell) if add_inputs: input_cell = Cell(id='InputCell', pynn_cell='SpikeSourcePoisson') input_cell.parameters = { 'start': 0, 'duration': 10000000000, 'rate': 150 } net.cells.append(input_cell) e_syn = Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_exp', parameters={'tau_syn': 0.5}) net.synapses.append(e_syn) i_syn = Synapse(id='gaba', pynn_receptor_type='inhibitory', pynn_synapse_type='curr_exp', parameters={'tau_syn': 0.5}) net.synapses.append(i_syn) N_full = { 'L23': { 'E': 20683, 'I': 5834 }, 'L4': { 'E': 21915, 'I': 5479 }, 'L5': { 'E': 4850, 'I': 1065 }, 'L6': { 'E': 14395, 'I': 2948 } } scale = 0.1 pops = [] input_pops = [] pop_dict = {} layers = ['L23'] layers = ['L23', 'L4', 'L5', 'L6'] for l in layers: i = 3 - layers.index(l) r = RectangularRegion(id=l, x=0, y=i * net.parameters['layer_height'], z=0, width=net.parameters['width'], height=net.parameters['layer_height'], depth=net.parameters['depth']) net.regions.append(r) for t in ['E', 'I']: try: import opencortex.utils.color as occ if l == 'L23': if t == 'E': color = occ.L23_PRINCIPAL_CELL if t == 'I': color = occ.L23_INTERNEURON if l == 'L4': if t == 'E': color = occ.L4_PRINCIPAL_CELL if t == 'I': color = occ.L4_INTERNEURON if l == 'L5': if t == 'E': color = occ.L5_PRINCIPAL_CELL if t == 'I': color = occ.L5_INTERNEURON if l == 'L6': if t == 'E': color = occ.L6_PRINCIPAL_CELL if t == 'I': color = occ.L6_INTERNEURON except: color = '.8 0 0' if t == 'E' else '0 0 1' pop_id = '%s_%s' % (l, t) pops.append(pop_id) ref = 'l%s%s' % (l[1:], t.lower()) exec( ref + " = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % ref) exec("net.populations.append(%s)" % ref) exec("pop_dict['%s'] = %s" % (pop_id, ref)) if add_inputs: color = '.8 .8 .8' input_id = '%s_%s_input' % (l, t) input_pops.append(input_id) input_ref = 'l%s%s_i' % (l[1:], t.lower()) exec( input_ref + " = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % input_ref) exec("net.populations.append(%s)" % input_ref) #l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':}) #l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id) #l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id) #net.populations.append(l23e) #net.populations.append(l23ei) #net.populations.append(l23i) #net.populations.append(l23ii) conn_probs = [ [0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0.], [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0.], [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.], [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0.], [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.], [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.], [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252], [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443] ] if add_inputs: for p in pops: proj = Projection(id='proj_input_%s' % p, presynaptic='%s_input' % p, postsynaptic=p, synapse=e_syn.id, delay=2, weight='input_weight') proj.one_to_one_connector = OneToOneConnector() net.projections.append(proj) for pre_i in range(len(pops)): for post_i in range(len(pops)): pre = pops[pre_i] post = pops[post_i] prob = conn_probs[post_i][pre_i] ####### TODO: check!!!! weight = 1 syn = e_syn if prob > 0: if 'I' in pre: weight = -1 syn = i_syn proj = Projection(id='proj_%s_%s' % (pre, post), presynaptic=pre, postsynaptic=post, synapse=syn.id, delay=1, weight=weight) proj.random_connectivity = RandomConnectivity(probability=prob) net.projections.append(proj) print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON recordTraces = {} recordSpikes = {} from neuromllite.utils import evaluate for p in pops: forecast_size = evaluate(pop_dict[p].size, net.parameters) recordTraces[p] = list(range(min(2, forecast_size))) recordSpikes[p] = '*' for ip in input_pops: recordSpikes[ip] = '*' sim = Simulation(id='Sim%s' % net.id, network=new_file, duration='100', dt='0.025', seed=1234, recordTraces=recordTraces, recordSpikes=recordSpikes) sim.to_json_file() return sim, net
input_source_e = InputSource(id='Exc_in', neuroml2_input='PulseGenerator', parameters={'amplitude':'exc_input', 'delay':'input_delay', 'duration':'input_duration'}) net.input_sources.append(input_source_e) net.inputs.append(Input(id='Exc_stim', input_source=input_source_e.id, population=pc_pop.id, percentage=100)) input_source_i = InputSource(id='Inh_in', neuroml2_input='PulseGenerator', parameters={'amplitude':'inh_input', 'delay':'input_delay', 'duration':'input_duration'}) net.input_sources.append(input_source_i) net.inputs.append(Input(id='Inh_stim', input_source=input_source_i.id, population=go_pop.id, percentage=100))''' # Save to JSON format new_file = net.to_json_file('%s.json' % net.id) sim = Simulation(id='Sim%s' % net.id, duration='100', dt='0.005', network=new_file, recordRates={'all': '*'}) sim.to_json_file('Sim%s.nmllite.json' % net.id) check_to_generate_or_run(sys.argv, sim)
pops = [exc_pop, inh_pop] internal_connections(pops, W, syns) # Save to JSON format net.id = 'Joglekar_figure1b' new_file = net.to_json_file('Joglekar_figure1c.json') sim = Simulation(id='SimJoglekar_figure1b', duration='2', dt='0.02', network=new_file, recordVariables={'r': { 'all': '*' }}) sim.to_json_file('SimJoglekar_figure1c.nmllite.json') check_to_generate_or_run(sys.argv, sim) # Open data file data = np.loadtxt('Excitatory_' + str(round(wEE, 5)) + '_' + str(round(wEI, 5)) + '_0.r.dat') maxamplocal[j, i] = np.max(data[:, 1]) for file in glob.glob('./*.{}'.format('dat')): print(file) shutil.move(file, './Files/') ### Abrir arquivo usando pandas e salvar o maximo em uma matriz se o autovalor for positivo else: # set value -5 to unstable region (it helps to plot the colormap)
def generate(): dt = 100 # ms, so 0.1s simtime = 5000 # ms, so 50s ################################################################################ ### Build new network net = Network(id="ABCD") net.notes = "Example of a simplified network" net.parameters = {"A_initial": 0.1, "A_slope": 2.2} cellInput = Cell(id="a_input", lems_source_file="PNL.xml", parameters={"variable": "A_initial"}) net.cells.append(cellInput) cellA = Cell(id="a", lems_source_file="PNL.xml") net.cells.append(cellA) cellB = Cell(id="b", lems_source_file="PNL.xml") net.cells.append(cellB) cellC = Cell(id="c", lems_source_file="PNL.xml") net.cells.append(cellC) cellD = Cell(id="d", lems_source_file="PNL.xml") net.cells.append(cellD) rsDL = Synapse(id="rsDL", lems_source_file="PNL.xml") net.synapses.append(rsDL) r1 = RectangularRegion(id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) pAin = Population( id="A_input", size="1", component=cellInput.id, properties={ "color": "0.2 0.2 0.2", "radius": 3 }, random_layout=RandomLayout(region=r1.id), ) net.populations.append(pAin) pA = Population( id="A", size="1", component=cellA.id, properties={ "color": "0 0.9 0", "radius": 5 }, random_layout=RandomLayout(region=r1.id), ) net.populations.append(pA) pB = Population( id="B", size="1", component=cellB.id, properties={ "color": ".8 .8 .8", "radius": 5 }, random_layout=RandomLayout(region=r1.id), ) net.populations.append(pB) pC = Population( id="C", size="1", component=cellC.id, properties={ "color": "0.7 0.7 0.7", "radius": 5 }, random_layout=RandomLayout(region=r1.id), ) net.populations.append(pC) pD = Population( id="D", size="1", component=cellD.id, properties={ "color": "0.7 0 0", "radius": 5 }, random_layout=RandomLayout(region=r1.id), ) net.populations.append(pD) silentDLin = Synapse(id="silentSyn_proj_input", lems_source_file="PNL.xml") net.synapses.append(silentDLin) net.projections.append( Projection( id="proj_input", presynaptic=pA.id, postsynaptic=pB.id, synapse=rsDL.id, pre_synapse=silentDLin.id, type="continuousProjection", weight=1, random_connectivity=RandomConnectivity(probability=1), )) silentDL0 = Synapse(id="silentSyn_proj0", lems_source_file="PNL.xml") net.synapses.append(silentDL0) net.projections.append( Projection( id="proj0", presynaptic=pAin.id, postsynaptic=pA.id, synapse=rsDL.id, pre_synapse=silentDL0.id, type="continuousProjection", weight=1, random_connectivity=RandomConnectivity(probability=1), )) silentDL1 = Synapse(id="silentSyn_proj1", lems_source_file="PNL.xml") net.synapses.append(silentDL1) net.projections.append( Projection( id="proj1", presynaptic=pA.id, postsynaptic=pC.id, synapse=rsDL.id, pre_synapse=silentDL1.id, type="continuousProjection", weight=1, random_connectivity=RandomConnectivity(probability=1), )) silentDL2 = Synapse(id="silentSyn_proj2", lems_source_file="PNL.xml") net.synapses.append(silentDL2) net.projections.append( Projection( id="proj2", presynaptic=pB.id, postsynaptic=pD.id, synapse=rsDL.id, pre_synapse=silentDL2.id, type="continuousProjection", weight=1, random_connectivity=RandomConnectivity(probability=1), )) silentDL3 = Synapse(id="silentSyn_proj3", lems_source_file="PNL.xml") net.synapses.append(silentDL3) net.projections.append( Projection( id="proj3", presynaptic=pC.id, postsynaptic=pD.id, synapse=rsDL.id, pre_synapse=silentDL3.id, type="continuousProjection", weight=1, random_connectivity=RandomConnectivity(probability=1), )) new_file = net.to_json_file("%s.json" % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation( id="Sim%s" % net.id, network=new_file, duration=simtime, dt=dt, seed=123, recordVariables={"OUTPUT": { "all": "*" }}, ) # ,'INPUT':{'all':'*'} sim.to_json_file() return sim, net
def generate(): ################################################################################ ### Build new network net = Network(id='ExampleK') net.notes = 'Example...' net.parameters = {'pop_size': '8', 'stim_amp': '0.3'} cell = Cell(id='kuramoto1', lems_source_file='CellExamples.xml') net.cells.append(cell) input_source = InputSource(id='iclamp0', neuroml2_input='PulseGeneratorDL', parameters={ 'amplitude': 'stim_amp', 'delay': '100ms', 'duration': '800ms' }) net.input_sources.append(input_source) ''' input_source = InputSource(id='poissonFiringSyn', neuroml2_input='poissonFiringSynapse', parameters={'average_rate':"eta", 'synapse':"ampa", 'spike_target':"./ampa"}) ''' r1 = RectangularRegion(id='region1', x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) pE = Population(id='Epop', size='pop_size', component=cell.id, properties={'color': '1 0 0'}, random_layout=RandomLayout(region=r1.id)) net.populations.append(pE) ''' net.synapses.append(Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_alpha', parameters={'tau_syn':0.1})) net.projections.append(Projection(id='projEinput', presynaptic=pEpoisson.id, postsynaptic=pE.id, synapse='ampa', delay=2, weight=0.02, one_to_one_connector=OneToOneConnector())) net.projections.append(Projection(id='projEE', presynaptic=pE.id, postsynaptic=pE.id, synapse='ampa', delay=2, weight=0.002, random_connectivity=RandomConnectivity(probability=.5))) net.projections.append(Projection(id='projEI', presynaptic=pE.id, postsynaptic=pI.id, synapse='ampa', delay=2, weight=0.02, random_connectivity=RandomConnectivity(probability=.5))) net.projections.append(Projection(id='projIE', presynaptic=pI.id, postsynaptic=pE.id, synapse='gaba', delay=2, weight=0.02, random_connectivity=RandomConnectivity(probability=.5))) ''' net.inputs.append( Input(id='stim', input_source=input_source.id, population=pE.id, percentage=50)) #print(net) #print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='SimExampleK', network=new_file, duration='1000', dt='0.025', seed=123, recordVariables={'sin_theta': { pE.id: '*' }}) sim.to_json_file() return sim, net
def generate(): dt = 0.1 simtime = 1 ################################################################################ ### Build new network net = Network(id='ABC') net.notes = 'Example of simplified network' net.parameters = {'A_initial': 0, 'A_slope': 5} cellInput = Cell(id='a_input', lems_source_file='PNL.xml', parameters={'variable': 'A_initial'}) net.cells.append(cellInput) cellA = Cell(id='a', lems_source_file='PNL.xml', parameters={'slope': 'A_slope'}) net.cells.append(cellA) cellB = Cell(id='b', lems_source_file='PNL.xml') net.cells.append(cellB) cellC = Cell(id='c', lems_source_file='PNL.xml') net.cells.append(cellC) rsDL = Synapse(id='rsDL', lems_source_file='PNL.xml') net.synapses.append(rsDL) r1 = RectangularRegion(id='region1', x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) pAin = Population(id='A_input', size='1', component=cellInput.id, properties={ 'color': '0.2 0.2 0.2', 'radius': 3 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pAin) pA = Population(id='A', size='1', component=cellA.id, properties={ 'color': '0 0.9 0', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pA) pB = Population(id='B', size='1', component=cellB.id, properties={ 'color': '.9 0 0', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pB) pC = Population(id='C', size='1', component=cellC.id, properties={ 'color': '0.7 0 0', 'radius': 5 }, random_layout=RandomLayout(region=r1.id)) net.populations.append(pC) silentDLin = Synapse(id='silentSyn_proj_input', lems_source_file='PNL.xml') net.synapses.append(silentDLin) net.projections.append( Projection(id='proj_input', presynaptic=pA.id, postsynaptic=pB.id, synapse=rsDL.id, pre_synapse=silentDLin.id, type='continuousProjection', weight=1, random_connectivity=RandomConnectivity(probability=1))) silentDL0 = Synapse(id='silentSyn_proj0', lems_source_file='PNL.xml') net.synapses.append(silentDL0) net.projections.append( Projection(id='proj0', presynaptic=pAin.id, postsynaptic=pA.id, synapse=rsDL.id, pre_synapse=silentDL0.id, type='continuousProjection', weight=1, random_connectivity=RandomConnectivity(probability=1))) silentDL1 = Synapse(id='silentSyn_proj1', lems_source_file='PNL.xml') net.synapses.append(silentDL1) net.projections.append( Projection(id='proj1', presynaptic=pA.id, postsynaptic=pC.id, synapse=rsDL.id, pre_synapse=silentDL1.id, type='continuousProjection', weight=1, random_connectivity=RandomConnectivity(probability=1))) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='Sim%s' % net.id, network=new_file, duration=simtime, dt=dt, seed=123, recordVariables={ 'OUTPUT': { 'all': '*' }, 'INPUT': { 'all': '*' } }) sim.to_json_file() return sim, net
def generate(): dt = 0.025 simtime = 1000 ################################################################################ ### Build new network net = Network(id="Example7_Brunel2000") net.notes = "Example 7: based on network of Brunel 2000" net.parameters = { "g": 4, "eta": 1, "order": 5, "epsilon": 0.1, "J": 0.1, "delay": 1.5, "tauMem": 20.0, "tauSyn": 0.1, "tauRef": 2.0, "U0": 0.0, "theta": 20.0, } cell = Cell(id="ifcell", pynn_cell="IF_curr_alpha") cell.parameters = { "tau_m": "tauMem", "tau_refrac": "tauRef", "v_rest": "U0", "v_reset": "U0", "v_thresh": "theta", "cm": 0.001, "i_offset": 0, } # cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml') net.cells.append(cell) poisson_input = Cell(id="poisson_input", pynn_cell="SpikeSourcePoisson") poisson_input.parameters = { "rate": "1000 * (eta*theta/(J*4*order*epsilon*tauMem)) * (4*order*epsilon)", "start": 0, "duration": 1e9, } net.cells.append(poisson_input) r1 = RectangularRegion(id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000) net.regions.append(r1) pE = Population( id="Epop", size="4*order", component=cell.id, properties={ "color": ".9 0 0", "radius": 5 }, random_layout=RandomLayout(region=r1.id), ) pEpoisson = Population( id="expoisson", size="4*order", component=poisson_input.id, properties={ "color": "0.9 0.7 0.7", "radius": 3 }, random_layout=RandomLayout(region=r1.id), ) pI = Population( id="Ipop", size="1*order", component=cell.id, properties={ "color": "0 0 .9", "radius": 5 }, random_layout=RandomLayout(region=r1.id), ) pIpoisson = Population( id="inpoisson", size="1*order", component=poisson_input.id, properties={ "color": "0.7 0.7 0.9", "radius": 3 }, random_layout=RandomLayout(region=r1.id), ) net.populations.append(pE) net.populations.append(pEpoisson) net.populations.append(pI) net.populations.append(pIpoisson) net.synapses.append( Synapse( id="ampa", pynn_receptor_type="excitatory", pynn_synapse_type="curr_alpha", parameters={"tau_syn": 0.1}, )) net.synapses.append( Synapse( id="gaba", pynn_receptor_type="inhibitory", pynn_synapse_type="curr_alpha", parameters={"tau_syn": 0.1}, )) delay_ext = dt downscale = 1 J_eff = "J*%s" % (downscale) # synaptic weights, scaled for alpha functions, such that # for constant membrane potential, charge J would be deposited fudge = 0.00041363506632638 # ensures dV = J at V=0 JE = "((%s)/tauSyn)*%s" % (J_eff, fudge) JI = "-1*g*%s" % (JE) net.projections.append( Projection( id="projEinput", presynaptic=pEpoisson.id, postsynaptic=pE.id, synapse="ampa", delay=delay_ext, weight=JE, one_to_one_connector=OneToOneConnector(), )) net.projections.append( Projection( id="projIinput", presynaptic=pIpoisson.id, postsynaptic=pI.id, synapse="ampa", delay=delay_ext, weight=JE, one_to_one_connector=OneToOneConnector(), )) net.projections.append( Projection( id="projEE", presynaptic=pE.id, postsynaptic=pE.id, synapse="ampa", delay="delay", weight=JE, random_connectivity=RandomConnectivity(probability="epsilon"), )) net.projections.append( Projection( id="projEI", presynaptic=pE.id, postsynaptic=pI.id, synapse="ampa", delay="delay", weight=JE, random_connectivity=RandomConnectivity(probability="epsilon"), )) net.projections.append( Projection( id="projIE", presynaptic=pI.id, postsynaptic=pE.id, synapse="gaba", delay="delay", weight=JI, random_connectivity=RandomConnectivity(probability="epsilon"), )) net.projections.append( Projection( id="projII", presynaptic=pI.id, postsynaptic=pI.id, synapse="gaba", delay="delay", weight=JI, random_connectivity=RandomConnectivity(probability="epsilon"), )) # print(net) # print(net.to_json()) new_file = net.to_json_file("%s.json" % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation( id="SimExample7", network=new_file, duration=simtime, dt=dt, seed=123, record_traces={ pE.id: [0, 1], pI.id: [0, 1] }, record_spikes={ pE.id: "*", pI.id: "*", pEpoisson.id: [0, 1, 2, 3, 4], pIpoisson.id: [0, 1, 2, 3, 4], }, ) sim.to_json_file() return sim, net
def generate(): ################################################################################ ### Build new network net = Network(id='Example7_Brunel2000') net.notes = 'Example 7: based on network of Brunel 2000' net.parameters = { 'g': 4, 'eta': 1, 'order': 5, 'epsilon': 0.1, 'J': 0.1, 'delay': 1.5, 'tauMem': 20.0, 'tauSyn': 0.1, 'tauRef': 2.0, 'U0': 0.0, 'theta': 20.0 } cell = Cell(id='ifcell', pynn_cell='IF_curr_alpha') cell.parameters = { 'tau_m': 'tauMem', 'tau_refrac': 'tauRef', 'v_rest': 'U0', 'v_reset': 'U0', 'v_thresh': 'theta', 'cm': 0.001, "i_offset": 0 } #cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml') net.cells.append(cell) expoisson = Cell(id='expoisson', pynn_cell='SpikeSourcePoisson') expoisson.parameters = { 'rate': '1000 * (eta*theta/(J*4*order*epsilon*tauMem)) * (4*order*epsilon)', 'start': 0, 'duration': 1e9 } net.cells.append(expoisson) ''' input_source = InputSource(id='iclamp0', pynn_input='DCSource', parameters={'amplitude':0.002, 'start':100., 'stop':900.}) input_source = InputSource(id='poissonFiringSyn', neuroml2_input='poissonFiringSynapse', parameters={'average_rate':"eta", 'synapse':"ampa", 'spike_target':"./ampa"}) net.input_sources.append(input_source)''' pE = Population(id='Epop', size='4*order', component=cell.id, properties={'color': '1 0 0'}) pEpoisson = Population(id='Einput', size='4*order', component=expoisson.id, properties={'color': '.5 0 0'}) pI = Population(id='Ipop', size='1*order', component=cell.id, properties={'color': '0 0 1'}) net.populations.append(pE) net.populations.append(pEpoisson) net.populations.append(pI) net.synapses.append( Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_alpha', parameters={'tau_syn': 0.1})) net.synapses.append( Synapse(id='gaba', pynn_receptor_type='inhibitory', pynn_synapse_type='curr_alpha', parameters={'tau_syn': 0.1})) net.projections.append( Projection(id='projEinput', presynaptic=pEpoisson.id, postsynaptic=pE.id, synapse='ampa', delay=2, weight=0.02, one_to_one_connector=OneToOneConnector())) ''' net.projections.append(Projection(id='projEE', presynaptic=pE.id, postsynaptic=pE.id, synapse='ampa', delay=2, weight=0.002, random_connectivity=RandomConnectivity(probability=.5)))''' net.projections.append( Projection(id='projEI', presynaptic=pE.id, postsynaptic=pI.id, synapse='ampa', delay=2, weight=0.02, random_connectivity=RandomConnectivity(probability=.5))) ''' net.projections.append(Projection(id='projIE', presynaptic=pI.id, postsynaptic=pE.id, synapse='gaba', delay=2, weight=0.02, random_connectivity=RandomConnectivity(probability=.5))) net.inputs.append(Input(id='stim', input_source=input_source.id, population=pE.id, percentage=50))''' #print(net) #print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='SimExample7', network=new_file, duration='1000', dt='0.025', seed=123, recordTraces={ pE.id: '*', pI.id: '*' }, recordSpikes={'all': '*'}) sim.to_json_file() return sim, net
####################### Save network in json file ############################# print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON sim = Simulation(id='SimJoglekar1Network', network=new_file, duration=duration, dt=dt, seed=1234, recordTraces={ pE.id: [0, 1], pI.id: [0, 1] }, recordSpikes={ pE.id: '*', pI.id: '*' }) sim.to_json_file() ################################################################################ ### Run in some simulators from neuromllite.NetworkGenerator import check_to_generate_or_run import sys check_to_generate_or_run(sys.argv, sim)
def generate(ref="Example6_PyNN", add_inputs=True): ################################################################################ ### Build new network net = Network(id=ref, notes="Another network for PyNN - work in progress...") net.parameters = { "N_scaling": 0.005, "layer_height": 400, "width": 100, "depth": 100, "input_weight": 0.1, } cell = Cell(id="CorticalCell", pynn_cell="IF_curr_exp") cell.parameters = { "cm": 0.25, # nF "i_offset": 0.0, # nA "tau_m": 10.0, # ms "tau_refrac": 2.0, # ms "v_reset": -65.0, # mV "v_rest": -65.0, # mV "v_thresh": -50.0, # mV } net.cells.append(cell) if add_inputs: input_cell = Cell(id="InputCell", pynn_cell="SpikeSourcePoisson") input_cell.parameters = {"start": 0, "duration": 10000000000, "rate": 150} net.cells.append(input_cell) e_syn = Synapse( id="ampa", pynn_receptor_type="excitatory", pynn_synapse_type="curr_exp", parameters={"tau_syn": 0.5}, ) net.synapses.append(e_syn) i_syn = Synapse( id="gaba", pynn_receptor_type="inhibitory", pynn_synapse_type="curr_exp", parameters={"tau_syn": 0.5}, ) net.synapses.append(i_syn) N_full = { "L23": {"E": 20683, "I": 5834}, "L4": {"E": 21915, "I": 5479}, "L5": {"E": 4850, "I": 1065}, "L6": {"E": 14395, "I": 2948}, } scale = 0.1 pops = [] input_pops = [] pop_dict = {} layers = ["L23"] layers = ["L23", "L4", "L5", "L6"] for l in layers: i = 3 - layers.index(l) r = RectangularRegion( id=l, x=0, y=i * net.parameters["layer_height"], z=0, width=net.parameters["width"], height=net.parameters["layer_height"], depth=net.parameters["depth"], ) net.regions.append(r) for t in ["E", "I"]: try: import opencortex.utils.color as occ if l == "L23": if t == "E": color = occ.L23_PRINCIPAL_CELL if t == "I": color = occ.L23_INTERNEURON if l == "L4": if t == "E": color = occ.L4_PRINCIPAL_CELL if t == "I": color = occ.L4_INTERNEURON if l == "L5": if t == "E": color = occ.L5_PRINCIPAL_CELL if t == "I": color = occ.L5_INTERNEURON if l == "L6": if t == "E": color = occ.L6_PRINCIPAL_CELL if t == "I": color = occ.L6_INTERNEURON except: color = ".8 0 0" if t == "E" else "0 0 1" pop_id = "%s_%s" % (l, t) pops.append(pop_id) ref = "l%s%s" % (l[1:], t.lower()) exec( ref + " = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % ref) exec("net.populations.append(%s)" % ref) exec("pop_dict['%s'] = %s" % (pop_id, ref)) if add_inputs: color = ".8 .8 .8" input_id = "%s_%s_input" % (l, t) input_pops.append(input_id) input_ref = "l%s%s_i" % (l[1:], t.lower()) exec( input_ref + " = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % input_ref) exec("net.populations.append(%s)" % input_ref) # l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':}) # l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id) # l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id) # net.populations.append(l23e) # net.populations.append(l23ei) # net.populations.append(l23i) # net.populations.append(l23ii) conn_probs = [ [0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0.0, 0.0076, 0.0], [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0.0, 0.0042, 0.0], [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.0], [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0.0, 0.1057, 0.0], [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.0], [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.0], [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252], [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443], ] if add_inputs: for p in pops: proj = Projection( id="proj_input_%s" % p, presynaptic="%s_input" % p, postsynaptic=p, synapse=e_syn.id, delay=2, weight="input_weight", ) proj.one_to_one_connector = OneToOneConnector() net.projections.append(proj) for pre_i in range(len(pops)): for post_i in range(len(pops)): pre = pops[pre_i] post = pops[post_i] prob = conn_probs[post_i][pre_i] ####### TODO: check!!!! weight = 1 syn = e_syn if prob > 0: if "I" in pre: weight = -1 syn = i_syn proj = Projection( id="proj_%s_%s" % (pre, post), presynaptic=pre, postsynaptic=post, synapse=syn.id, delay=1, weight=weight, ) proj.random_connectivity = RandomConnectivity(probability=prob) net.projections.append(proj) print(net.to_json()) new_file = net.to_json_file("%s.json" % net.id) ################################################################################ ### Build Simulation object & save as JSON record_traces = {} record_spikes = {} from neuromllite.utils import evaluate for p in pops: forecast_size = evaluate(pop_dict[p].size, net.parameters) record_traces[p] = list(range(min(2, forecast_size))) record_spikes[p] = "*" for ip in input_pops: record_spikes[ip] = "*" sim = Simulation( id="Sim%s" % net.id, network=new_file, duration="100", dt="0.025", seed=1234, record_traces=record_traces, record_spikes=record_spikes, ) sim.to_json_file() return sim, net