{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial: Multi-Cell, Single Population Network (with BioNet)\n", "\n", "In this tutorial, we will create a more complex network that contains multiple biophysical cells of the same cell-type (we will cover heterogenous networks in the next tutorial). The network will contain recurrent connections, as well as external input that provides input to the network.\n", "\n", "**Note** - scripts and files for running this tutorial can be found in the directory [sources/chapter03/](https://github.com/AllenInstitute/bmtk/tree/develop/docs/tutorial/sources/chapter03)\n", "\n", "requirements:\n", "* bmtk\n", "* NEURON 7.4+\n", "\n", "For more information on the BioNet Simulator, please see the [BioNet Overview](https://alleninstitute.github.io/bmtk/bionet.html).\n", "\n", "For more information on BMTK and SONATA format, please see the [Overview of BMTK](https://alleninstitute.github.io/bmtk/user_guide.html) and [Network Builder](https://alleninstitute.github.io/bmtk/builder.html) pages." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Building the Network\n", "\n", "First we will build our internal network, which consists of 100 different cells. All the cells are of the same type but they all have a different location and y-axis rotation. \n", "\n", "### Set nodes " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from bmtk.builder.networks import NetworkBuilder\n", "from bmtk.builder.auxi.node_params import positions_columinar, xiter_random\n", "\n", "cortex = NetworkBuilder('mcortex')\n", "cortex.add_nodes(\n", " N=100,\n", " pop_name='Scnn1a',\n", " positions=positions_columnar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),\n", " rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),\n", " rotation_angle_zaxis=3.646878266,\n", " potental='exc',\n", " model_type='biophysical',\n", " model_template='ctdb:Biophys1.hoc',\n", " model_processing='aibs_perisomatic',\n", " dynamics_params='472363762_fit.json',\n", " morphology='Scnn1a_473845048_m.swc'\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New parameters:\n", "\n", "* *N* - Indicates the number of cells in our population. \n", "* *positions* - Sets the spatial location of each cell in the network. Here we define it by the `positions_columnar` built-in method, which will randomly place our cells in a column (users can define their own positions as shown here). \n", "* *rotation_angle_yaxis* - Sets the y angle for each cell. Here it is defined by a built-in function `xiter_random` that randomly assigns each cell a given y angle. \n", "* *rotation_angle_zaxis* - Sets the z angle for each cell. Here we assign a single value to each cell. \n", "\n", "See [tutorial 1](tutorial_01_single_cell_clamped.ipynb) for a description of the other parameters.\n", "\n", "**Note** - Here we define the y-angle (that is, the angle or rotation around the y-axis) by a function which returns a lists of values, but the z-angle is defined by a single value. This means that all cells will share the z-angle. we could alteratively give all cells the same y-rotation angle:\n", "\n", "```python\n", " rotation_angle_yaxis=rotation_value\n", "```\n", "or give all cells a unique z-rotation angle:\n", "```python\n", " rotation_angle_zaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi)\n", "```\n", "and in general, it is at the discretion of the modeler to choose what parameters are unique to each cell, and what parameters are global to the cell-type." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set edges\n", "\n", "Next we want to add recurrent edges. To create the connections we will use the built-in `distance_connector` function, which will assign the number of connections between two cells randomly (in the range *nsyn_min* and *nsysn_max*) but weighted by distance. The other parameters, including the synaptic model (*AMPA_ExcToExc*) will be shared by all connections.\n", "\n", "To use this, or to use customized connection functions, we must pass in the name of our connection function using the \"connection_rule\" parameter, and the function parameters through \"connection_params\" as a dictionary, which will looks something like:\n", "```python\n", " connection_rule=\n", " connection_params={'param_arg1': val1, 'param_arg2': val2, ...}\n", "```\n", "The connection_rule method isn't explicitly called by the script. Rather when the build() method is called, the connection_rule will iterate through every source/target node pair, and use the rule to build a connection matrix.\n", "\n", "See [tutorial 2](tutorial_02_single_cell_syn.ipynb) for a description of the other parameters.\n", "\n", "\n", "After building the connections based on our connection function, we will save the nodes and edges files into the network/directory." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bmtk.builder.auxi.edge_connectors import distance_connector\n", "\n", "cortex.add_edges(\n", " source={'pop_name': 'Scnn1a'}, target={'pop_name': 'Scnn1a'},\n", " connection_rule=distance_connector,\n", " connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 50.0, 'nsyn_min': 0, 'nsyn_max': 10},\n", " syn_weight=2.0e-04,\n", " distance_range=[30.0, 150.0],\n", " target_sections=['basal', 'apical', 'soma'],\n", " delay=2.0,\n", " dynamics_params='AMPA_ExcToExc.json',\n", " model_template='exp2syn'\n", ")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the main network" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cortex.build()\n", "cortex.save_nodes(output_dir='sim_ch03/network')\n", "cortex.save_edges(output_dir='sim_ch03/network')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the input network\n", "\n", "After building our internal network, we will build the external thalamic network which will provide input (See [tutorial 2](tutorial_02_single_cell_syn.ipynb) for more details). Our thalamic network will consist of 100 \"filter\" cells, which aren't actual cells but rather just place holders for spike-trains." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "thalamus = NetworkBuilder('mthalamus')\n", "thalamus.add_nodes(\n", " N=100,\n", " pop_name='tON',\n", " potential='exc',\n", " model_type='virtual'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The input network doesn't have recurrent connections, rather all the cells are connected in a feedforward fashion onto the internal network. To connect the networks we create edges with the thalamus nodes as the sources and the cortex nodes as the targets. This time we use the built-in `connect_random` connection rule, which will randomly assign each thalamus --> cortex connection between 0 and 12 synapses.\n", "\n", "**Note** - If building the input network in a separate script you would need to reload the saved mcortex network cell files using the import function." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from bmtk.builder.auxi.edge_connectors import connect_random\n", "\n", "thalamus.add_edges(\n", " source=thalamus.nodes(), target=cortex.nodes(),\n", " connection_rule=connect_random,\n", " connection_params={'nsyn_min': 0, 'nsyn_max': 12},\n", " syn_weight=5.0e-05, \n", " distance_range=[0.0, 150.0],\n", " target_sections=['basal', 'apical'],\n", " delay=2.0,\n", " dynamics_params='AMPA_ExcToExc.json',\n", " model_template='exp2syn'\n", ")\n", "\n", "thalamus.build()\n", "thalamus.save_nodes(output_dir='sim_ch03/network')\n", "thalamus.save_edges(output_dir='sim_ch03/network')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spike Trains\n", "\n", "Next we need to create the individual spike trains for our thalamic filter cells. We will use a Poisson distribution of spikes for our 300 cells, each firing at ~ 15 Hz over a 3 second window. Then we can save our spike trains as a [SONATA file](https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#spike-file) under the **sim_ch03/inputs** directory. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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node_idstimestampspopulation
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" ], "text/plain": [ " node_ids timestamps population\n", "0 0 87.943425 mthalamus\n", "1 0 110.660282 mthalamus\n", "2 0 140.897319 mthalamus\n", "3 0 170.834006 mthalamus\n", "4 0 174.629060 mthalamus" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bmtk.utils.reports.spike_trains import PoissonSpikeGenerator\n", "\n", "psg = PoissonSpikeGenerator(population='mthalamus')\n", "psg.add(node_ids=range(100), # Have 10 nodes to match mthalamus\n", " firing_rate=15.0, # 15 Hz, we can also pass in a nonhomogeneous function/array\n", " times=(0.0, 3.0)) # Firing starts at 0 s up to 3 s\n", "psg.to_sonata('sim_ch03/inputs/mthalamus_spikes.h5')\n", "\n", "# Let's do a quick check that we have reasonable results. Should see somewhere on the order of 15*3*100 = 4500\n", "# spikes\n", "psg.to_dataframe().head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Setting up BioNet\n", "\n", "### File structure\n", "\n", "Before running a simulation, we will need to create the runtime environment, including parameter files, run-script and configuration files. If you've already completed [tutorial 2](tutorial_02_single_cell_syn.ipynb) you can just copy the files to **sim_ch03** (just make sure not to overwrite the **network** and **inputs** directory).\n", "\n", "Or create them from scratch by either running the command:\n", "```bash\n", "$ python -m bmtk.utils.sim_setup \\\n", " --report-vars v,cai \\ \n", " --network sim_ch03/network \\ \n", " --spikes-inputs mthalamus:sim_ch03/inputs/mthalamus_spikes.h5 \\\n", " --dt 0.1 \\\n", " --tstop 3000.0 \\ \n", " --include-examples \\\n", " --compile-mechanisms \\ \n", " bionet sim_ch03\n", "```\n", "\n", "Or call the function directly in python:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from bmtk.utils.sim_setup import build_env_bionet\n", "\n", "build_env_bionet(\n", " base_dir='sim_ch03',\n", " config_file='config.json',\n", " network_dir='sim_ch03/network',\n", " tstop=3000.0, dt=0.1,\n", " report_vars=['v', 'cai'], # Record membrane potential and calcium (default soma)\n", " spikes_inputs=[('mthalamus', # Name of population which spikes will be generated for\n", " 'sim_ch03/inputs/mthalamus_spikes.h5')],\n", " include_examples=True, # Copies components files\n", " compile_mechanisms=True # Will try to compile NEURON mechanisms\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's a good idea to check the configuration files **sim_ch03/circuit_config.json** and **sim_ch03/simulation_config.json**, especially to make sure that bmtk will know to use our generated spikes file (if you don't see the below section in the simulation_config.json file, go ahead and add it). \n", "\n", "```json\n", "{\n", " \"inputs\": {\n", " \"tc_spikes\": {\n", " \"input_type\": \"spikes\",\n", " \"module\": \"csv\",\n", " \"input_file\": \"${BASE_DIR}/mthalamus_spikes.csv\",\n", " \"node_set\": \"mthalamus\"\n", " }\n", " }\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Running the simulation\n", "\n", "Once our config file is setup we can run a simulation either through the command line:\n", "```bash\n", "$ cd sim_ch03\n", "$ python run_bionet.py config.json\n", "```\n", "\n", "or through the script:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:44:46,934 [INFO] Created log file\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Created log file\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "numprocs=1\n", "2022-08-09 21:44:47,012 [INFO] Building cells.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Building cells.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:44:54,838 [INFO] Building recurrent connections\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Building recurrent connections\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:44:55,189 [INFO] Building virtual cell stimulations for mthalamus_spikes\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Building virtual cell stimulations for mthalamus_spikes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:44:58,773 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Running simulation for 3000.000 ms with the time step 0.100 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:44:58,774 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Starting timestep: 0 at t_sim: 0.000 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:44:58,775 [INFO] Block save every 5000 steps\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Block save every 5000 steps\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:45:26,516 [INFO] step:5000 t_sim:500.00 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils: step:5000 t_sim:500.00 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:45:54,497 [INFO] step:10000 t_sim:1000.00 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils: step:10000 t_sim:1000.00 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:46:26,947 [INFO] step:15000 t_sim:1500.00 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils: step:15000 t_sim:1500.00 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:46:56,036 [INFO] step:20000 t_sim:2000.00 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils: step:20000 t_sim:2000.00 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:47:24,824 [INFO] step:25000 t_sim:2500.00 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils: step:25000 t_sim:2500.00 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:47:55,361 [INFO] step:30000 t_sim:3000.00 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils: step:30000 t_sim:3000.00 ms\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2022-08-09 21:47:55,393 [INFO] Simulation completed in 2.0 minutes, 56.62 seconds \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:NEURONIOUtils:Simulation completed in 2.0 minutes, 56.62 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "\n", "conf = bionet.Config.from_json('sim_ch03/config.json')\n", "conf.build_env()\n", "net = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=net)\n", "sim.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Analyzing the run\n", "\n", "If successful, we should have our results in the **output** directory. We can use the analyzer to plot a raster of the spikes over time:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.spike_trains import plot_raster\n", "\n", "\n", "_ = plot_raster(config_file='sim_ch03/config.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our config file we used the *cell_vars* and *node_id_selections* parameters to save the calcium influx and membrane potential of selected cells. We can also use the analyzer to display these traces:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "\n", "_ = plot_traces(config_file='sim_ch03/config.json', report_name='v_report')\n", "_ = plot_traces(config_file='sim_ch03/config.json', report_name='v_report', node_ids=[50])\n", "_ = plot_traces(config_file='sim_ch03/config.json', report_name='cai_report')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Additional Information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customized node parameters\n", "\n", "When building our cortex nodes, we used some built-in functions to set certain parameters like positions and y-axis rotations:\n", "```python\n", "cortex.add_nodes(N=100,\n", " pop_name='Scnn1a',\n", " positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),\n", " rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),\n", " ...\n", "```\n", "\n", "These functions will assign every cell a unique value in the *positions* and *rotation_angle_yaxis* parameters, unlike the *pop_name* parameter which will be the same for all 100 cells. We can verify by the following code:\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cell 0: pop_name: Scnn1a, positions: [-12.67936906 16.99325185 -8.20277884], angle_yaxis: 5.127788634883025\n", "cell 1: pop_name: Scnn1a, positions: [ -1.22804411 91.62129377 -11.58852979], angle_yaxis: 1.0965697825257734\n" ] } ], "source": [ "cortex_nodes = list(cortex.nodes())\n", "n0 = cortex_nodes[0]\n", "n1 = cortex_nodes[1]\n", "print('cell 0: pop_name: {}, positions: {}, angle_yaxis: {}'.format(n0['pop_name'], n0['positions'], n0['rotation_angle_yaxis']))\n", "print('cell 1: pop_name: {}, positions: {}, angle_yaxis: {}'.format(n1['pop_name'], n1['positions'], n1['rotation_angle_yaxis']))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Network Builder contains a growing number of built-in functions. However for advanced networks a modeler will probably want to assign parameters using their own functions. To do so, a modeler only needs to pass in, or alternatively create a function that returns, a list of size *N*. When saving the network, each individual position will be saved in the **nodes.h5** file assigned to each cell by a unique ID number.\n", "\n", "```python\n", "def cortex_positions(N):\n", " # codex to create a list/numpy array of N (x, y, z) positions.\n", " return [...]\n", "\n", "cortex.add_nodes(N=100,\n", " positions=cortex_positions(100),\n", " ...\n", "```\n", "\n", "or if we wanted we could give all cells the same position (the builder has no restrictions on this, however this may cause issues if you're trying to create connections based on distance). When saving the network, the same position is assigned as a global cell-type property, and thus saved in the **node_types.csv** file.\n", "```python\n", "cortex.add_nodes(N=100,\n", " positions=np.ndarray([100.23, -50.67, 89.01]),\n", " ...\n", "```\n", "\n", "We can use the same logic not just for *positions* and *rotation_angle*, but for any parameter we choose." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customized connector functions\n", "\n", "When creating edges, we used the built-in `distance_connector` function to help create the connection matrix. There are a number of built-in connection functions, but we also allow modelers to create their own. To do so, the modeler must create a function that takes in a source, target, and a variable number of parameters, and pass back a natural number representing the number of connections.\n", "\n", "The Builder will iterate over that function, passing in every source/target node pair (filtered by the source and target parameters in `add_edges()`). The source and target parameters are essentially dictionaries that can be used to fetch properties of the nodes. A typical example would look like:\n", "\n", "```python\n", "def customized_connector(source, target, param1, param2, param3):\n", " if source.node_id == target.node_id:\n", " # necessary if we don't want autapses\n", " return 0\n", " source_pot = source['potential']\n", " target_pot = target['potential']\n", " # some code to determine number of connections\n", " return n_synapses\n", " \n", "...\n", "cortex.add_edges(source=, target=,\n", " connection_rule=customized_connector,\n", " connection_params={'param1': , 'param2': , 'param3': },\n", " ...\n", "```" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 }