-
Notifications
You must be signed in to change notification settings - Fork 70
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #485 from ModECI/nml_examples
Latest neuroml with PyNN based examples & graphs
- Loading branch information
Showing
33 changed files
with
2,054 additions
and
20 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 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) |
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Oops, something went wrong.