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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,202 @@ | ||
| from neuromllite import ( | ||
| Network, | ||
| Cell, | ||
| InputSource, | ||
| Population, | ||
| Synapse, | ||
| RectangularRegion, | ||
| RandomLayout, | ||
| ) | ||
| from neuromllite import Projection, RandomConnectivity, Input, Simulation | ||
| import sys | ||
|
|
||
|
|
||
| def generate( | ||
| ref, | ||
| num_pop0=2, | ||
| num_pop1=2, | ||
| conn_prob=1, | ||
| conn_weight=0.3, | ||
| input_percentage=50, | ||
| input_weight=".8", | ||
| pynn_cell="IF_curr_alpha", | ||
| ): | ||
|
|
||
| ################################################################################ | ||
| ### Build new network | ||
|
|
||
| net = Network(id=ref) | ||
| net.notes = "Example: %s" % ref | ||
| net.parameters = {"input_amp": 0.99} | ||
|
|
||
| cell = Cell(id="testcell", pynn_cell=pynn_cell) | ||
|
|
||
| cell.parameters = {"i_offset": 0.0} | ||
| if "IF_" in pynn_cell: | ||
| cell.parameters["tau_refrac"] = 5 | ||
| else: | ||
| cell.parameters = {"i_offset": 0.05} | ||
| net.parameters = {"input_amp": 0} | ||
| net.cells.append(cell) | ||
|
|
||
| input_source = InputSource( | ||
| id="i_clamp", | ||
| pynn_input="DCSource", | ||
| parameters={"amplitude": "input_amp", "start": 200.0, "stop": 800.0}, | ||
| ) | ||
| net.input_sources.append(input_source) | ||
|
|
||
| r1 = RectangularRegion( | ||
| id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000 | ||
| ) | ||
| net.regions.append(r1) | ||
|
|
||
| p0 = Population( | ||
| id="pop0", | ||
| size=num_pop0, | ||
| component=cell.id, | ||
| properties={"color": "1 0 0", "radius": 20}, | ||
| random_layout=RandomLayout(region=r1.id), | ||
| ) | ||
| net.populations.append(p0) | ||
|
|
||
| if num_pop1 > 0: | ||
| p1 = Population( | ||
| id="pop1", | ||
| size=num_pop1, | ||
| component=cell.id, | ||
| properties={"color": "0 1 0", "radius": 20}, | ||
| random_layout=RandomLayout(region=r1.id), | ||
| ) | ||
| net.populations.append(p1) | ||
|
|
||
| """p2 = Population( | ||
| id="pop2", | ||
| size=1, | ||
| component=cell2.id, | ||
| properties={"color": "0 0 1", "radius": 20}, | ||
| random_layout=RandomLayout(region=r1.id), | ||
| ) | ||
| net.populations.append(p2)""" | ||
|
|
||
| net.synapses.append( | ||
| Synapse( | ||
| id="ampaSyn", | ||
| pynn_receptor_type="excitatory", | ||
| pynn_synapse_type="curr_alpha", | ||
| parameters={"tau_syn": 20}, | ||
| ) | ||
| ) | ||
| """net.synapses.append( | ||
| Synapse( | ||
| id="gabaSyn", | ||
| pynn_receptor_type="inhibitory", | ||
| pynn_synapse_type="cond_alpha", | ||
| parameters={"e_rev": -80, "tau_syn": 10}, | ||
| ) | ||
| )""" | ||
|
|
||
| if num_pop1 > 0: | ||
| net.projections.append( | ||
| Projection( | ||
| id="proj0", | ||
| presynaptic=p0.id, | ||
| postsynaptic=p1.id, | ||
| synapse="ampaSyn", | ||
| delay=2, | ||
| weight=conn_weight, | ||
| ) | ||
| ) | ||
| net.projections[0].random_connectivity = RandomConnectivity( | ||
| probability=conn_prob | ||
| ) | ||
|
|
||
| """net.projections.append( | ||
| Projection( | ||
| id="proj1", | ||
| presynaptic=p0.id, | ||
| postsynaptic=p2.id, | ||
| synapse="gabaSyn", | ||
| delay=2, | ||
| weight=0.01, | ||
| ) | ||
| ) | ||
| net.projections[1].random_connectivity = RandomConnectivity(probability=1)""" | ||
|
|
||
| net.inputs.append( | ||
| Input( | ||
| id="stim", | ||
| input_source=input_source.id, | ||
| population=p0.id, | ||
| percentage=input_percentage, | ||
| weight=input_weight, | ||
| ) | ||
| ) | ||
|
|
||
| net_json_file = net.to_json_file("%s.json" % net.id) | ||
| net_yaml_file = net.to_yaml_file("%s.yaml" % net.id) | ||
|
|
||
| ################################################################################ | ||
| ### Build Simulation object & save as JSON | ||
|
|
||
| sim = Simulation( | ||
| id="Sim%s" % net.id, | ||
| network=net_json_file, | ||
| duration="1000", | ||
| dt="0.01", | ||
| record_traces={"all": "*"}, | ||
| record_spikes={"all": "*"} if "IF_" in pynn_cell else {}, | ||
| ) | ||
|
|
||
| sim.to_json_file() | ||
| sim.network = net_yaml_file | ||
| sim.to_yaml_file("%s.yaml" % sim.id) | ||
|
|
||
| sim.network = net_json_file # reverting, for call below... | ||
|
|
||
| return sim, net | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
|
|
||
| if "-one" in sys.argv: | ||
| sim, net = generate("OneCell", num_pop0=1, num_pop1=0, input_percentage=100) | ||
| elif "-hh" in sys.argv: | ||
| sim, net = generate( | ||
| "HH", num_pop0=1, num_pop1=0, input_percentage=0, pynn_cell="HH_cond_exp" | ||
| ) | ||
| elif "-input_weights" in sys.argv: | ||
| sim, net = generate( | ||
| "InputWeights", | ||
| num_pop0=4, | ||
| num_pop1=0, | ||
| input_percentage=62, | ||
| input_weight=".8*random()", | ||
| ) | ||
| elif "-simple_net" in sys.argv: | ||
| sim, net = generate( | ||
| "SimpleNet", | ||
| num_pop0=1, | ||
| num_pop1=1, | ||
| input_percentage=100, | ||
| input_weight="1", | ||
| ) | ||
| elif "-net1" in sys.argv: | ||
| sim, net = generate( | ||
| "Net1", | ||
| num_pop0=2, | ||
| num_pop1=3, | ||
| conn_weight="random()", | ||
| input_percentage=100, | ||
| input_weight="2*random()", | ||
| ) | ||
| else: | ||
| sim, net = generate("All") | ||
|
|
||
| ################################################################################ | ||
| ### Run in some simulators | ||
|
|
||
| from neuromllite.NetworkGenerator import check_to_generate_or_run | ||
|
|
||
| check_to_generate_or_run(sys.argv, sim) |
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