def run(argv):
sim = ncs.Simulation()
excitatory_parameters = sim.addNeuron("label_excitatory",
"izhikevich",
{
"a": 0.2,
"b": 0.2,
"c": -65.0,
"d": ncs.Uniform(7.0, 9.0),
"u": ncs.Uniform(-15.0, -11.0),
"v": ncs.Normal(-60.0, 5.0),
"threshold": 30,
}
)
group_1 = sim.addNeuronGroup("group_1", 100, "label_excitatory", None) # last param is geometry
group_2 = sim.addNeuronGroup("group_2", 100, excitatory_parameters)
all_cells = sim.addNeuronAlias("all_cells", [group_1, "group_2"])
sim.addNeuronAlias("all", all_cells)
sim.addNeuronAlias("all_2", "all_cells")
flat_parameters = sim.addSynapse("flat_synapse",
"flat",
{ "delay": 2,
"current": ncs.Normal(18.0,2.0)
})
all_to_all = sim.addSynapseGroup("all_to_all", all_cells, "all_2", 0.1, flat_parameters)
all_to_all_2 = sim.addSynapseGroup("all_to_all_2",
[group_1, group_2],
"all_2",
0.1,
flat_parameters)
one_to_two = sim.addSynapseGroup("one_to_two",
group_1,
"group_2",
0.1,
"flat_synapse")
all_connections = sim.addSynapseAlias("all_connections", [all_to_all, one_to_two])
if not sim.init(argv):
print "Failed to initialize simulation."
return
sim.addStimulus("rectangular_current", { "amplitude": 18.0 }, group_1, 0.1, 0.0, 1.0)
sim.addStimulus("rectangular_current", { "amplitude": 18.0 }, "all", 0.1, 0.0, 0.5)
sim.addStimulus("rectangular_current", { "amplitude": 18.0 }, "group_2", 0.1, 0.1, 1.1)
voltage_report = sim.addReport("group_1", "neuron", "neuron_voltage", 1.0,0.0,0.01)
voltage_report.toAsciiFile("/tmp/foo.txt")
fire_report = sim.addReport(all_cells, "neuron", "neuron_fire", 1.0,0.0,0.01)
fire_report.toStdOut()
sim.run(duration=0.01)
return
def Run(argv):
sim = ncs.Simulation()
excitatory_parameters = sim.addNeuron(
"label_excitatory", "izhikevich", {
"a": 0.2,
"b": 0.2,
"c": -65.0,
"d": ncs.Uniform(7.0, 9.0),
"u": ncs.Uniform(-15.0, -11.0),
"v": ncs.Normal(-60.0, 5.0),
"threshold": 30,
})
group_1 = sim.addNeuronGroup("group_1", 1, "label_excitatory",
None) # last param is geometry
group_2 = sim.addNeuronGroup("group_2", 1, excitatory_parameters)
flat_parameters = sim.addNeuron(
"synapse", "ncs", {
"utilization": ncs.Normal(0.5, 0.05),
"redistribution": 1.0,
"last_prefire_time": 0.0,
"last_postfire_time": 0.0,
"tau_facilitation": 0.001,
"tau_depression": 0.001,
"tau_ltp": 0.015,
"tau_ltd": 0.03,
"A_ltp_minimum": 0.003,
"A_ltd_minimum": 0.003,
"max_conductance": 0.004,
"reversal_potential": 0.0,
"tau_postsynaptic_conductance": 0.025,
"psg_waveform_duration": 0.05,
"delay": ncs.Uniform(1, 5),
})
synapse = sim.addSynapseGroup("1_to_2", group_1, "group_2", 1,
flat_parameters)
if not sim.init(argv):
print "Failed to initialize simulation."
return
sim.addStimulus("rectangular_current", {"amplitude": 18.0}, group_1, 1.0,
0.0, 1.0)
#current_report = sim.addReport(group_2, "neuron", "synaptic_current", 1.0)
current_report = sim.addReport(group_2, "neuron", "synaptic_current", 1.0,
0.0, 0.05)
current_report.toStdOut()
sim.run(duration=0.05)
return
def add(sim):
# TODO(rvhoang): these values aren't yet accurate
synEE_PCR = {
"utilization": ncs.Normal(0.5, 0.05),
"redistribution": 1.0,
"last_prefire_time": 0.0,
"last_postfire_time": 0.0,
"tau_facilitation": 0.0,
"tau_depression": 0.0,
"tau_ltp": 0.015,
"tau_ltd": 0.03,
"A_ltp_minimum": 0.003,
"A_ltd_minimum": 0.003,
"max_conductance": 0.004,
"reversal_potential": 0.0,
"tau_postsynaptic_conductance": 0.025,
"psg_waveform_duration": 0.05,
"delay": ncs.Uniform(1,5),
}
synEI_PCR = {
"utilization": ncs.Normal(0.5, 0.05),
"redistribution": 1.0,
"last_prefire_time": 0.0,
"last_postfire_time": 0.0,
"tau_facilitation": 0.0,
"tau_depression": 0.0,
"tau_ltp": 0.005,
"tau_ltd": 0.005,
"A_ltp_minimum": 0.02,
"A_ltd_minimum": 0.01,
"max_conductance": 0.004,
"reversal_potential": 0.0,
"tau_postsynaptic_conductance": 0.025,
"psg_waveform_duration": 0.05,
"delay": ncs.Uniform(1,5),
}
synIE_PCR = {
"utilization": ncs.Normal(0.5, 0.05),
"redistribution": 1.0,
"last_prefire_time": 0.0,
"last_postfire_time": 0.0,
"tau_facilitation": 0.0,
"tau_depression": 0.0,
"tau_ltp": 0.005,
"tau_ltd": 0.005,
"A_ltp_minimum": 0.02,
"A_ltd_minimum": 0.01,
"max_conductance": 0.004,
"reversal_potential": 0.0,
"tau_postsynaptic_conductance": 0.025,
"psg_waveform_duration": 0.05,
"delay": ncs.Uniform(1,5),
}
synII_PCR = {
"utilization": ncs.Normal(0.5, 0.05),
"redistribution": 1.0,
"last_prefire_time": 0.0,
"last_postfire_time": 0.0,
"tau_facilitation": 0.0,
"tau_depression": 0.0,
"tau_ltp": 0.005,
"tau_ltd": 0.005,
"A_ltp_minimum": 0.02,
"A_ltd_minimum": 0.01,
"max_conductance": 0.004,
"reversal_potential": 0.0,
"tau_postsynaptic_conductance": 0.025,
"psg_waveform_duration": 0.05,
"delay": ncs.Uniform(1,5),
}
sim.addModelParameters("synEE_PCR", "ncs", synEE_PCR)
sim.addModelParameters("synEI_PCR", "ncs", synEI_PCR)
sim.addModelParameters("synIE_PCR", "ncs", synIE_PCR)
sim.addModelParameters("synII_PCR", "ncs", synII_PCR)
def run(argv):
#ncs.simulation() is required
sim = ncs.Simulation()
#start writing model - biology information
#addNeuron function
#Parameters for addNeuron function:
# 1. A neuron name (string)
# 2. A neuron type (string)
# izhikevich, ncs, or hh
# 3. A map for parameter names to their values(Generators)
# Generators can be exact, Normal, uniform
# exact example : "a": 0.02
# uniform example: "a": ncs.Uniform(min, max)
# normal example: "a": ncs.Normal(mean, standard deviation)
# Example of addNeuron with regular_spiking izhikevich neuron
regular_spiking_parameters = sim.addNeuron("regular_spiking","izhikevich",
{
"a": 0.02,
"b": 0.2,
"c": -65.0,
"d": 8.0,
"u": -12.0,
"v": -60.0,
"threshold": 30,
})
#addSynapse function
#Parameters for addSynapse function
# 1. A synapse name (string)
# 2. A synapse type (string)
# ncs or flat
# 3. A map for parameter names to their values (Generators)
ncs_synapse_parameters = sim.addSynapse("flat_synapse","ncs",{
"utilization": ncs.Normal(0.5,0.05),
"redistribution": 1.0,
"last_prefire_time": 0.0,
"last_postfire_time": 0.0,
"tau_facilitation": 0.001,
"tau_depression": 0.001,
"tau_ltp": 0.015,
"tau_ltd": 0.03,
"A_ltp_minimum": 0.003,
"A_ltd_minimum": 0.003,
"max_conductance": 0.3,
"reversal_potential":0.0,
"tau_postsynaptic_conductance": 0.02,
"psg_waveform_duration": 0.05,
"delay": 1,
})
#addNeuronGroup function
#Parameters for addNeuronGroup function:
# 1. A name of the group (string)
# 2. Number of cells (integer)
# 3. Neuron parameters
# 4. Geometry generator (optional)
group_1=sim.addNeuronGroup("group_1",1,regular_spiking_parameters,None)
group_2=sim.addNeuronGroup("group_2",1,regular_spiking_parameters,None)
#addSynapseGroup function
#Parameters for addSynapseGroup function:
# 1. A name of the connection
# 2. Presynaptic NeuronAlias or NeuronGroup
# 3. Postsynaptic NeuronAlias or NeuronGroup
# 4. Probability of connection
# 5. Parameters for synapse
connection1=sim.addSynapseGroup("connection1","group_1","group_2",1,"flat_synapse");
#initialize simulation
if not sim.init(argv):
print "failed to initialize simulation."
return
#addStimulus function
#parameters for addStimulus function:
# 1. A stimulus type (string)
# rectangular_current, rectangular_voltage, linear_current, linear_voltage,
# sine_current, or sine_voltage
# 2. A map from parameter names (strings) to their values (Generators)
# Parameter names are amplitude, starting_amplitude, ending_amplitude,
# delay, current, amplitude_scale, time_scale, phase, amplitude_shift,
# etc. based on the stimulus type
# 3. A set of target neuron groups
# 4. probability of a neuron receiving input
# 5. start time for stimulus (seconds)
# For example, if you wanted to start stimulus at 1 ms, write 0.01
# 6. end time for stimulus (seconds)
sim.addStimulus("rectangular_current",{"amplitude":10},group_1,1,0.01,1.0)
#addReport function
#Parameters for addReport function:
# 1. A set of neuron group or a set of synapse group to report on
# 2. A target type: "neuron" or "synapses"
# 3. type of report: synaptic_current, neuron_voltage, neuron_fire,
# input current, etc.
# 4. Probability (the percentage of elements to report on)
voltage_report_1=sim.addReport([group_1,group_2],"neuron","neuron_voltage",1.0,0.0,1.0)
#An example of a report to file
voltage_report_1.toAsciiFile("reg_voltage_report.txt");
#duration (in seconds) - each time step is 1 ms
sim.run(duration=1.0)
return
def run(argv):
sim = ncs.Simulation()
excitatory_parameters = sim.addNeuron("label_excitatory",
"izhikevich",
{
"a": 0.2,
"b": 0.2,
"c": -65.0,
"d": ncs.Uniform(7.0, 9.0),
"u": ncs.Uniform(-15.0, -11.0),
"v": ncs.Normal(-60.0, 5.0),
"threshold": 30
}
)
group_1 = sim.addNeuronGroup("group_1", 100, "label_excitatory", None) # last param is geometry
group_2 = sim.addNeuronGroup("group_2", 100, excitatory_parameters)
all_cells = sim.addNeuronAlias("all_cells", [group_1, "group_2"])
sim.addNeuronAlias("all", all_cells)
sim.addNeuronAlias("all_2", "all_cells")
flat_parameters = sim.addSynapse("synapse",
"ncs",
{ "utilization": ncs.Normal(0.5, 0.05),
"redistribution": 1.0,
"last_prefire_time": 0.0,
"last_postfire_time": 0.0,
"tau_facilitation": 0.001,
"tau_depression": 0.001,
"tau_ltp": 0.015,
"tau_ltd": 0.03,
"A_ltp_minimum": 0.003,
"A_ltd_minimum": 0.003,
"max_conductance": 0.004,
"reversal_potential": 0.0,
"tau_postsynaptic_conductance": 0.025,
"psg_waveform_duration": 0.05,
"delay": ncs.Uniform(1,5)
})
all_to_all = sim.addSynapseGroup("all_to_all", all_cells, "all_2", 0.1, flat_parameters)
all_to_all_2 = sim.addSynapseGroup("all_to_all_2",
[group_1, group_2],
"all_2",
0.1,
flat_parameters)
one_to_two = sim.addSynapseGroup("one_to_two",
group_1,
"group_2",
0.1,
"synapse")
all_connections = sim.addSynapseAlias("all_connections", [all_to_all, one_to_two])
if not sim.init(argv):
print "Failed to initialize simulation."
return
sim.addStimulus("rectangular_current", { "amplitude": 18.0 }, group_1, 0.1, 0.0, 0.1)
sim.addStimulus("rectangular_current", { "amplitude": 18.0 }, "all", 0.1, 0.0, 0.2)
sim.addStimulus("rectangular_current", { "amplitude": 18.0 }, "group_2", 0.1, 0.1, 0.3)
voltage_report = sim.addReport("group_1", "neuron", "neuron_voltage", 1.0, 0.0, 0.01)
voltage_report.toAsciiFile("foo.txt")
fire_report = sim.addReport(all_cells, "neuron", "neuron_fire", 1.0, 0.0, 0.01)
fire_report.toStdOut()
current_report = sim.addReport(all_cells, "neuron", "synaptic_current", 1.0, 0.0, 0.01)
current_report.toStdOut()
sim.run(duration=0.5)
del sim
return