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adding brunel network from paper
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‎docs/examples/biophys_components/synaptic_models/brunel_excitatory.json‎

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{
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"level_of_detail": "exp2syn",
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"tau1": 0.1,
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"tau2": 2.0,
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"erev": 0.0
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}

‎docs/examples/biophys_components/synaptic_models/brunel_inhibitory.json‎

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{
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"level_of_detail": "exp2syn",
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"tau1": 0.1,
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"tau2": 2.0,
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"erev": -80.0
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}
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‎docs/examples/ei_networks/README.md‎

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# The EI-network
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Network loosely based on the model described in "Dynamics of sparsely connected networks of excitatory
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and inhibitory spiking" (Brunel, 2000). Contains 12,000 cells across two populations, one population of excitatory
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neurons and another of inhibitory neurons. This network provides the same network with comparable dynamics using
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multi-compartmental models (BioNet), glif point-neuron models (PointNet), and population models (PopNet).
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## Running the models
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The scripts for building the network and running the simulations can be found in each simulators subdirectory. To
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build and run a simulation for BioNet:
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```bash
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$ cd bionet
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$ python build_network.py
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$ python run_bionet.py config.json
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```
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And for PointNet (or PopNet) replace "bionet" with "pointnet" (or "popnet").
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## Analyzing the results
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Unless changed in the *config.json* file, simulation results will be saved to the *output* directory by default. BioNet
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and PointNet will produce spike-trains for each neuron. While PopNet will produce the firing-rate dynamics of the
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two populations.
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‎docs/examples/ei_networks/bionet/README.md‎

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# Excitatory/Inhibitory Network for BioNet
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## Requirements
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* BMTK
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* Neuron 7.4+
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## Files
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* network/ - Circuit file of E/I cells and external network. If folder doesn't exists use the build_network.py script.
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* inputs/ - Spike-train files corresponding to the external network used to drive the simulation.
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* config.json - simulation and network parameters.
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* build_network.py - Used to build the network/ files.
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* build_input.py - Used to build the inputs/ file(s).
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* run_bionet.py - Script for running the network simulation.
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## Building the Network
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The Network model files must be built for running the simulation using the build_network.py script. It may take
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anywhere between 5-10 minutes to complete and will save all required files in network/ folder.
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```bash
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$ python build_network.py
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```
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## Running the Simulation
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You can use config.json to change parameters for simulating the network. By default everything recorded during the
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simulation is saved in the output/ directory, including a running log.txt to keep track of progress.
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```bash
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$ python run_bionet.py config.json
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```
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The network may have problems running on a single computer core. It is recommended to use a computing cluster, or
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if running on a single machnie use MPI to run on multtple cores:
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```bash
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$ mpirun -n <N> nrniv -mpi -python run_bionet.py config.json
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```

‎docs/examples/ei_networks/bionet/build_input.py‎

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import os
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import numpy as np
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import h5py
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from bmtk.utils.io.spike_trains import PoissonSpikesGenerator
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from bmtk.builder.aux.node_params import positions_columinar, xiter_random
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#input_ids = [n.node_id for n in inputNetwork.nodes()]
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input_h5 = h5py.File('network/external_nodes.h5','r')
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node_ids = np.array(input_h5['/nodes/external/node_id'],dtype = np.uint)
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input_ids = node_ids.tolist()
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from bmtk.utils.io.spike_trains import PoissonSpikesGenerator
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# Create a Poisson Spike train for all input nodes that fire at a rate of 0.5Hz.
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# The time units below is in milliseconds
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psg = PoissonSpikesGenerator(gids=input_ids, firing_rate = 2.2, tstart=0.0, tstop=3000.0)
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# Save the spike trains
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if not os.path.exists('network/source_input/'):
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os.makedirs('network/source_input/')
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psg.to_hdf5(file_name='inputs/poission_r1000_10in2p2.h5')

‎docs/examples/ei_networks/bionet/build_network.py‎

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import os
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import numpy as np
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from bmtk.builder import NetworkBuilder
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from bmtk.utils.io.spike_trains import PoissonSpikesGenerator
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from bmtk.builder.auxi.node_params import positions_columinar, xiter_random
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from bmtk.analyzer import nodes_table
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# Brunel network parameters -------------------------------------------------------------------------
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N_exc = 10000 # number of excitatory LIF neurons 80%
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N_inh = 2500 # number of inhibitory LIF neurons 20%
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n_cells = N_exc + N_inh # total number of internal neurons
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N_ext = 100 # number of neurons in the external network
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JE = 0.1 # excitatory synaptic strength (mV)
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g = 5. # ratio of inhibitory synpatic strength to excitatory synaptic strength (unitless)*************
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JI = g*JE # inhibitory synpatic strength (mv)
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eps = 0.1 # percentage of all possible target neurons that are connected to any given source
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CE = int(eps*N_exc) # number of internal connections with excitatory source neurons
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C_ext = CE # number of connections to each internal cell from the input network
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n_syn = int(C_ext/N_ext) # number of synapses per neuron in the input network
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CI = int(eps*N_inh) # number of internal connections with inhibitory source neurons
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C = CE + CI # number of internal connections
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tau_e = 20. # membrane time constant of excitatory neurons (ms)
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theta = 20. # neuron firing threshold (mV)
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Vr = 10. # neuron rest potential (mV)
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tau_rp = 2. # refractory period (ms)
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D = 1.5 # transmission delay (ms)
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v_ext_v_thr_ratio = 100. # ratio of v_ext to v_thr (unitless)***************
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eps_ext = C_ext/float(n_cells) # external connection probability
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v_thr = theta/(JE*CE*tau_e) # frequency needed for a neuron to reach threshold in absence of feedback
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v_ext = v_ext_v_thr_ratio*v_thr # external firing frequency
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#-----------------------------------------------------------------------------------------------
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def generate_random_positions(N):
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'''
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Generate N random positions.
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N: number of positions to generate
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'''
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x = np.random.random(N) # x-axis location
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y = np.random.random(N) # y-axis location
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z = np.random.random(N) # z-axis location
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positions = np.column_stack((x, y, z))
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return positions
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build_recurrent_edges = True
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bio_models = {
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"Internal_exc": {
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'N': N_exc,
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'model_type' :'biophysical',
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'model_name': 'Scnn1a', 'ei': 'e',
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'morphology': 'Scnn1a_473845048_m.swc',
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'model_template': 'nml:Cell_472363762.cell.nml'
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},
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"Internal_inh": {
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'N': N_inh,
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'model_type' :'biophysical',
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'model_name': 'PV1', 'ei': 'i',
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'morphology': 'Pvalb_470522102_m.swc',
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'model_template': 'nml:Cell_472912177.cell.nml'
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}
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}
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internal = NetworkBuilder("internal")
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for model in bio_models:
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params = bio_models[model].copy()
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internal.add_nodes(**params)
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#internal.save_nodes(nodes_file_name='internal_nodes.h5', node_types_file_name='internal_node_types.csv',output_dir='network')
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def random_connections(source,target, p = 0.1 ):
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sid = source['node_id'] # Get source id
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tid = target['node_id'] # Get target id
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# Avoid self-connections.
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if (sid == tid):
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if sid % 1000 == 0:
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print(sid)
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return None
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return np.random.binomial(1,p) #nsyns
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if build_recurrent_edges:
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# exc --> exc connections
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internal.add_edges(source={'ei': 'e'}, target={'ei': 'e'},
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connection_rule=random_connections,
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connection_params={'p': eps},
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dynamics_params='brunel_excitatory.json',
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model_template='Exp2Syn',
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syn_weight=7.0e-5,
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delay=D,
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target_sections=['basal','apical'],
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distance_range=[0.0, 1e+20])
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# exc --> inh connections
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internal.add_edges(source={'ei': 'e'}, target={'ei': 'i'},
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connection_rule=random_connections,
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connection_params={'p': eps},
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dynamics_params='brunel_excitatory.json',
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model_template='Exp2Syn',
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syn_weight=7.0e-5,
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delay=D,
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target_sections=['somatic','basal'],
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distance_range=[0.0, 1e+20])
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# inh --> exc connections
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internal.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'biophysical'},
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connection_rule=random_connections,
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connection_params={'p': eps},
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dynamics_params='brunel_inhibitory.json',
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model_template='Exp2Syn',
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syn_weight=17.5e-5,
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delay=D,
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target_sections=['somatic','basal','apical'],
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distance_range=[0.0, 200])
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# inh --> inh connections
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internal.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'biophysical'},
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connection_rule=random_connections,
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connection_params={'p': eps},
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dynamics_params='brunel_inhibitory.json',
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model_template='Exp2Syn',
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syn_weight=17.5e-5,
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delay=D,
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target_sections=['somatic','basal'],
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distance_range=[0.0, 1e+20])
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internal.build()
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print('Saving internal')
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internal.save(output_dir='network')
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print('Building external connections')
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external = NetworkBuilder("external")
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external.add_nodes(N=N_ext, model_type='virtual', ei='e')
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cm = external.add_edges(source=external.nodes(),
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target=internal.nodes(),
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connection_rule=random_connections,
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connection_params={'p': 0.1},
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dynamics_params='AMPA_ExcToExc.json',
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model_template='Exp2Syn',
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syn_weight = 7.0e-3,
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delay=D,
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target_sections = ['somatic','basal'],
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distance_range=[0.0, 1e+20])
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external.build()
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print('Saving external')
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external.save(output_dir='network')
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‎docs/examples/ei_networks/bionet/config.json‎

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{
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"manifest": {
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"$BASE_DIR": "${configdir}",
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"$OUTPUT_DIR": "$BASE_DIR/output",
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"$INPUT_DIR": "$BASE_DIR/inputs",
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"$NETWORK_DIR": "$BASE_DIR/network",
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"$COMPONENT_DIR": "$BASE_DIR/../../biophys_components"
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},
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"run": {
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"tstop": 3000.0,
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"dt": 0.1,
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"dL": 20.0,
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"spike_threshold": -15,
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"nsteps_block": 5000
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},
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"target_simulator":"NEURON",
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"conditions": {
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"celsius": 34.0,
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"v_init": -80
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},
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"inputs": {
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"external_spikes": {
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"input_type": "spikes",
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"module": "h5",
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"input_file": "$INPUT_DIR/poission_r1000_10in6.h5",
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"node_set": "external"
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}
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},
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"output":{
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"log_file": "$OUTPUT_DIR/log.txt",
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"output_dir": "$OUTPUT_DIR",
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"spikes_file": "$OUTPUT_DIR/spikes.h5",
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"spikes_file_csv": "$OUTPUT_DIR/spikes.csv"
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},
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"node_sets": {
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"bio_cells": {
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"model_type": "biophysical"
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}
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},
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"reports": {
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"membrane_potential": {
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"cells": [0,2500,5000,7500,10000,12499],
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"variable_name": "v",
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"module": "membrane_report",
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"file_name": "$OUTPUT_DIR/cell_vars.h5",
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"sections": "soma",
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"enabled": true
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}
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},
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"components": {
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"morphologies_dir": "$COMPONENT_DIR/morphologies",
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"synaptic_models_dir": "$COMPONENT_DIR/synaptic_models",
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"mechanisms_dir":"$COMPONENT_DIR/mechanisms",
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"biophysical_neuron_models_dir": "$COMPONENT_DIR/biophysical_neuron_templates/nml",
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"point_neuron_models_dir": "$COMPONENT_DIR/point_neuron_templates"
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},
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"networks": {
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"nodes": [
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{
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"nodes_file": "$NETWORK_DIR/internal_nodes.h5",
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"node_types_file": "$NETWORK_DIR/internal_node_types.csv"
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},
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{
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"nodes_file": "$NETWORK_DIR/external_nodes.h5",
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"node_types_file": "$NETWORK_DIR/external_node_types.csv"
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}
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],
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"edges": [
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{
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"edges_file": "$NETWORK_DIR/internal_internal_edges.h5",
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"edge_types_file": "$NETWORK_DIR/internal_edge_types.csv"
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},
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{
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"edges_file": "$NETWORK_DIR/external_internal_edges.h5",
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"edge_types_file": "$NETWORK_DIR/external_edge_types.csv"
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}
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]
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}
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}

‎docs/examples/ei_networks/bionet/inputs/poission_r1000_10in6.h5‎

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‎docs/examples/ei_networks/bionet/run_bionet.py‎

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import sys, os
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from bmtk.simulator import bionet
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def run(config_file):
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conf = bionet.Config.from_json(config_file, validate=True)
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conf.build_env()
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net = bionet.BioNetwork.from_config(conf)
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sim = bionet.BioSimulator.from_config(conf, network=net)
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sim.run()
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bionet.nrn.quit_execution()
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from bmtk.analyzer.visualization.spikes import plot_spikes, plot_rates
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plot_spikes('network/nodes.h5', 'network/node_types.csv', 'output/spikes.h5', save_as = 'rasters/plot.png',group_key='model_name')
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if __name__ == '__main__':
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if __file__ != sys.argv[-1]:
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run(sys.argv[-1])
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else:
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run('config_A.json')
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# run('config_je1e80g3jext5e-4_10in50.json')
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