Adding PointNet example with plastic synapses (and recordings)

examples/point_stdp/README.md

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+# PointNet simulation with plastic synapses
+
+
+## Running:
+To run a simulation, install bmtk and run the following:
+```
+$ python run_pointnet.py config.simulation_iclamp.json
+```
+If successful, will create a *output* directory containing log, spike trains and recorded cell variables.
+
+## The Network:
+The network files have already been built and stored as SONATA files in the *network/* directory. The bmtk Builder
+script used to create the network files is *build_network.py*. To adjust the parameters and/or topology of the network
+change this file and run:
+```
+$ python build_network.py
+```
+This will overwrite the existing files in the network directory. Note that there is some randomness in how the network
+is built, so expect (slightly) different simulation results everytime the network is rebuilt.
+
+## Simulation Parameters
+Parameters to run the simulation, including run-time, inputs, recorded variables, and networks are stored in config.json
+and can modified with a text editor.
+
+## Plotting results
+```
+$ python plot_output.py config.simulation_iclamp.json
+```
+
+## Perturbation simulations
+The file ```config_pertubration.json``` uses the same network (recurrent connections + feedforward inputs) as before. However
+is designed to also simulate the effects of optogenetic or current clamp inhibition and excitation on a subset of the cells. 
+To run this example:
+```bash
+$ python run_pointnet.py config_perturbations.json
+``` 
+
+The only difference between this simulation and the original is in the **inputs** section in the config. To simulate the 
+perturbations we use a current clamp input to make cells 20 through 39 overly excitatory:
+```json
+"exc_perturbation": {
+   "input_type": "current_clamp",
+   "module": "IClamp",
+   "node_set": {
+     "population": "cortex",
+     "node_ids": [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
+   },
+   "amp": 230.0,
+   "delay": 1.0,
+   "duration": 3000.0
+},
+```
+
+For cells 40 through 49 we can use a negative current to depress the cell activity
+```json
+"inh_perturbation": {
+  "input_type": "current_clamp",
+  "module": "IClamp",
+  "node_set": {
+    "population": "cortex",
+    "node_ids": [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]
+  },
+  "amp": -230.0,
+  "delay": 1.0,
+  "duration": 3000.0
+}
+```

examples/point_stdp/build_network.py

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+import os, sys
+
+from bmtk.builder import NetworkBuilder
+from bmtk.utils.reports.spike_trains import PoissonSpikeGenerator
+
+
+def build_network():
+    # target cells recieve inputs with plastic synapses
+    net_sources = NetworkBuilder('targets')
+    net_sources.add_nodes(
+        N=3,
+        model_type='point_neuron',
+        model_template='nest:iaf_psc_delta',
+        dynamics_params='iaf_psc_delta_exc.json'
+    )
+    net_sources.build()
+    net_sources.save(output_dir='network')
+
+    # source cells that synapse onto targets
+    net_feedforward = NetworkBuilder('sources')
+    net_feedforward.add_nodes(
+        N=2,
+        model_type='point_neuron',
+        model_template='nest:iaf_psc_delta',
+        dynamics_params='iaf_psc_delta_exc.json'
+    )
+
+    # connection 1: cells 0 --> 0
+    net_feedforward.add_edges(
+        source=net_feedforward.nodes(node_id=0),
+        target=net_sources.nodes(node_id=0),
+        connection_rule=1,
+        syn_weight=15.0,
+        delay=1.0,
+        dynamics_params='stdp_exc_1.json',
+        model_template='stdp_synapse'
+    )
+
+    # connection: cells 1 --> 1
+    net_feedforward.add_edges(
+        source=net_feedforward.nodes(node_id=1),
+        target=net_sources.nodes(node_id=1),
+        connection_rule=1,
+        syn_weight=15.0,
+        delay=1.0,
+        dynamics_params='stdp_exc_2.json',
+        model_template='stdp_synapse'
+    )
+
+    # connection: cells [0, 1] --> 2
+    net_feedforward.add_edges(
+        source=net_feedforward.nodes(),
+        target=net_sources.nodes(node_id=2),
+        connection_rule=1,
+        syn_weight=15.0,
+        delay=1.0,
+        dynamics_params='stdp_exc_3.json',
+        model_template='stdp_synapse'
+    )
+
+    net_feedforward.build()
+    net_feedforward.save(output_dir='network')
+
+    # virtual inputs into feedforward cells
+    net_virts = NetworkBuilder('virtual')
+    net_virts.add_nodes(
+        N=10,
+        model_type='virtual'
+    )
+    net_virts.add_edges(
+        target=net_feedforward.nodes(),
+        connection_rule=1,
+        syn_weight=4.2,
+        delay=1.5,
+        dynamics_params='static_exc.json',
+        model_template='static_synapse'
+    )
+    net_virts.build()
+    net_virts.save(output_dir='network')
+
+
+def generate_virt_spikes():
+    psg = PoissonSpikeGenerator()
+    psg.add(node_ids='network/virtual_nodes.h5', firing_rate=20.0, times=(0.0, 5.0), population='virtual')
+    psg.to_sonata('inputs/virtual_spikes.h5')
+
+
+if __name__ == '__main__':
+    build_network()
+    generate_virt_spikes()

examples/point_stdp/components/cell_models/iaf_psc_delta_exc.json

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+{
+
+}

examples/point_stdp/components/synaptic_models/static_exc.json

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+{
+}

examples/point_stdp/components/synaptic_models/stdp_exc_1.json

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+{
+}

examples/point_stdp/components/synaptic_models/stdp_exc_2.json

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+{
+}

examples/point_stdp/components/synaptic_models/stdp_exc_3.json

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+{
+}

examples/point_stdp/config.simulation_iclamp.json

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+{
+  "manifest": {
+	"$BASE_DIR": "${configdir}",
+    "$NETWORK_DIR": "$BASE_DIR/network",
+    "$MODELS_DIR": "$BASE_DIR/components",
+    "$OUTPUT_DIR": "$BASE_DIR/output_iclamp"
+  },
+
+  "run": {
+    "tstop": 5000.0,
+    "dt": 0.01
+  },
+
+  "inputs": {
+    "current_clamp_steps": {
+      "input_type": "current_clamp",
+      "module": "IClamp",
+      "node_set": {"population": "sources", "node_id": [0]},
+      "amp": [380.0, 500.0, 600.0],
+      "delay": [500.0, 2000.0, 3500.0],
+      "duration": [1000.0, 1000.0, 1000.0]
+    },
+    "current_clamp_1": {
+      "input_type": "current_clamp",
+      "module": "IClamp",
+      "node_set": {"population": "sources", "node_id": [1]},
+      "amp": 450.0,
+      "delay": 500.0,
+      "duration": 4000.0
+    }
+  },
+
+  "reports": {
+    "membrane_potential": {
+      "cells": {
+        "population": "sources"
+      },
+      "variable_name": "V_m",
+      "module": "multimeter_report",
+      "sections": "soma",
+      "enabled": true
+    },
+    "weight_recorder_1": {
+      "module": "weight_recorder",
+      "nest_model": "stdp_synapse",
+      "model_template": "stdp_synapse_recorder",
+      "file_name": "stdp_weights.csv",
+      "enabled": true
+    }
+  },
+
+  "output": {
+	  "log_file": "log.txt",
+	  "spikes_file": "spikes.h5",
+    "spikes_file_csv": "spikes.csv",
+	  "output_dir": "$OUTPUT_DIR",
+    "overwrite_output_dir": true,
+    "quiet_simulator": true
+  },
+
+  "target_simulator":"NEST",
+
+  "components": {
+    "point_neuron_models_dir": "$MODELS_DIR/cell_models",
+	  "synaptic_models_dir": "$MODELS_DIR/synaptic_models"
+  },
+
+  "networks": {
+    "nodes": [
+      {
+        "nodes_file": "$NETWORK_DIR/targets_nodes.h5",
+        "node_types_file": "$NETWORK_DIR/targets_node_types.csv"
+      },
+      {
+        "nodes_file": "$NETWORK_DIR/sources_nodes.h5",
+        "node_types_file": "$NETWORK_DIR/sources_node_types.csv"
+      }
+    ],
+    "edges": [
+      {
+        "edges_file": "$NETWORK_DIR/sources_targets_edges.h5",
+        "edge_types_file": "$NETWORK_DIR/sources_targets_edge_types.csv"
+      }
+    ]
+  }
+}

examples/point_stdp/config.simulation_virtual.json

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+{
+  "manifest": {
+	"$BASE_DIR": "${configdir}",
+    "$NETWORK_DIR": "$BASE_DIR/network",
+    "$MODELS_DIR": "$BASE_DIR/components",
+    "$OUTPUT_DIR": "$BASE_DIR/output_virtual",
+    "$INPUT_DIR": "$BASE_DIR/inputs"
+  },
+
+  "run": {
+    "tstop": 5000.0,
+    "dt": 0.01
+  },
+
+  "inputs": {
+    "thalamus_spikes": {
+      "input_type": "spikes",
+      "module": "sonata",
+      "input_file": "$INPUT_DIR/virtual_spikes.h5",
+      "node_set": "virtual"
+    }
+  },
+
+  "reports": {
+    "membrane_potential": {
+      "cells": {
+        "population": "sources"
+      },
+      "variable_name": "V_m",
+      "module": "multimeter_report",
+      "sections": "soma",
+      "enabled": true
+    },
+    "weight_recorder_1": {
+      "module": "weight_recorder",
+      "nest_model": "stdp_synapse",
+      "model_template": "stdp_synapse_recorder",
+      "file_name": "stdp_weights.csv",
+      "enabled": true
+    }
+  },
+
+  "output": {
+	  "log_file": "log.txt",
+	  "spikes_file": "spikes.h5",
+    "spikes_file_csv": "spikes.csv",
+	  "output_dir": "$OUTPUT_DIR",
+    "overwrite_output_dir": true,
+    "quiet_simulator": true
+  },
+
+  "target_simulator":"NEST",
+
+  "components": {
+    "point_neuron_models_dir": "$MODELS_DIR/cell_models",
+	  "synaptic_models_dir": "$MODELS_DIR/synaptic_models"
+  },
+
+  "networks": {
+    "nodes": [
+      {
+        "nodes_file": "$NETWORK_DIR/targets_nodes.h5",
+        "node_types_file": "$NETWORK_DIR/targets_node_types.csv"
+      },
+      {
+        "nodes_file": "$NETWORK_DIR/sources_nodes.h5",
+        "node_types_file": "$NETWORK_DIR/sources_node_types.csv"
+      },
+      {
+        "nodes_file": "$NETWORK_DIR/virtual_nodes.h5",
+        "node_types_file": "$NETWORK_DIR/virtual_node_types.csv"
+      }
+    ],
+    "edges": [
+      {
+        "edges_file": "$NETWORK_DIR/sources_targets_edges.h5",
+        "edge_types_file": "$NETWORK_DIR/sources_targets_edge_types.csv"
+      },
+      {
+        "edges_file": "$NETWORK_DIR/virtual_sources_edges.h5",
+        "edge_types_file": "$NETWORK_DIR/virtual_sources_edge_types.csv"
+      }
+    ]
+  }
+}

examples/point_stdp/network/sources_node_types.csv

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+node_type_id model_template model_type dynamics_params
+100 nest:iaf_psc_delta point_neuron iaf_psc_delta_exc.json

examples/point_stdp/network/sources_targets_edge_types.csv

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+edge_type_id target_query source_query delay model_template syn_weight dynamics_params
+100 node_id=='0' node_id=='0' 1.0 stdp_synapse_recorder 15.0 stdp_exc_1.json
+101 node_id=='1' node_id=='1' 1.0 stdp_synapse_recorder 15.0 stdp_exc_2.json
+102 node_id=='2' * 1.0 stdp_synapse_recorder 15.0 stdp_exc_1.json

examples/point_stdp/network/targets_node_types.csv

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+node_type_id model_template model_type dynamics_params
+100 nest:iaf_psc_delta point_neuron iaf_psc_delta_exc.json

examples/point_stdp/network/virtual_node_types.csv

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+node_type_id model_type
+100 virtual

examples/point_stdp/network/virtual_sources_edge_types.csv

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+edge_type_id target_query source_query delay model_template syn_weight dynamics_params
+100 * * 1.5 static_synapse 4.2 static_exc.json

examples/point_stdp/plot_output.py

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+import os, sys
+import matplotlib.pyplot as plt
+
+from bmtk.analyzer.compartment import plot_traces
+from bmtk.analyzer.spike_trains import plot_raster
+
+
+def plot_results(config_file):
+    plot_raster(config_file=config_file, show=False, population='sources')
+    plot_raster(config_file=config_file, show=False, population='targets')
+    plot_traces(config_file=config_file, report_name='membrane_potential')
+    plt.show()
+
+
+if __name__ == '__main__':
+    if os.path.basename(__file__) != sys.argv[-1]:
+        plot_results(sys.argv[-1])
+    else:
+        plot_results('config.simulation_iclamp.json')