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Simulating example networks with BioNet
=======================================

To get started with running simulations with BioNet, we recommend to run network examples provided in the directory examples/simulators/bionet:

* /14cells : network of 14 cell receiving an external input.
* /450cells : network of 450 cells receiving an external input

Working through the sections below, you will learn about contents of folders in each example and how to use them to run a simulation.

Configuring simulation
-----------------------

All the files listed below and describing the network are listed in the configuration file `config.json` to instruct BioNet where to look for files describing network. Please note that all directory and file names can be adjusted by users as they wish, as long as those names are reflected in the config file. Additionally, the configuration file includes simulation parameters (e.g. duration of simulation and time step, etc).

Please refer to the `configuration file documentation <./bionet_config.html>`_ for details.


Model description
-----------------

The directory `network` contains files that describe parameters of the cells and their connections.

Cells in modeled networks may be generally categorized as: 1) the simulated cells for which dynamics is explicitly simulated using NEURON and 2) external input cells for which spiking dynamics is not simulated explicitly, but instead loaded from files. The external input cells can influence the simulated cells, but not vice versa.  Thus, such an abstraction is convenient for simulating feedforward inputs.

The example networks include the simulated cells from primary mouse visual cortex (V1) receiving external inputs from the Lateral Geniculate Nucleu (LGN) and also background input in a form of a Travelling Wave (TW) as shown in the Figure 1 below:

.. image:: _static/images/all_network_cropped.png
   :scale: 15 %

**Figure 1**. Cells in the example network. Simulated network (V1) includes 14 cells: 10 biophysically detailed (depicted as morphological reconstructions) and 4 Leaky Integrate-and-fire (LIF) (showed as circles). Simulated V1 cells have recurrent connections (hollow arrow) as well as receive feedforward external input (solid arrows) in the form of spikes from the LGN (red circles) and TW cells (dark orange circles).

A network can be viewed as a graph. Description of a graph needs to specify the properties of the nodes (cells) and edges (connections).  In BioNet (and more generally, in BMTK), each node (edge) belongs to a particular node (edge) type, thus possessing all the properties of that type, but also possessing properties specific to that individual node (edge) instance. Thus, properties of nodes are described using two files: node_file and node_type_file. Similarly edges are described using two files: edges_file, edge_types_file. For node_type_file and edge_type_file we use csv format, whereas for node_file and edge_file — HDF5 format.


**Nodes files:**

+------------------------+-----------------+----------------------+
|Cells                   | Nodes           | Node types           |
+========================+=================+======================+
| V1 cells               | v1_nodes.h5     | v1_node_types.csv    |
+------------------------+-----------------+----------------------+
| LGN cells              | lgn_nodes.h5    | lgn_node_types.csv   |
+------------------------+-----------------+----------------------+
| TW cells               | tw_nodes.h5     | tw_node_types.csv    |
+------------------------+-----------------+----------------------+


**Edges files:**

+------------------------+-----------------+----------------------+
|Connections             | Edges           | Edge types           |
+========================+=================+======================+
| V1 -> V1 connections   | v1_v1_edges.h5  | v1_v1_edge_types.csv |
+------------------------+-----------------+----------------------+
| LGN -> V1 connections  | lgn_v1_edges.h5 | lgn_v1_edge_types.csv|
+------------------------+-----------------+----------------------+
| TW->V1 connections     | tw_v1_edges.h5  | tw_v1_edge_types.csv |
+------------------------+-----------------+----------------------+


Please refer to the `network file format documentation <./network_file_formats.html>`_ for details.

The spikes times of external cells are precomuted and provided in the directory examples/simulator/NWB_files.

Each network utilizes models of individual cells, synapses and recording electrodes defined in the examples/simulators/bionet/components/mechanisms directory.

The paths to each of these files and directories are specified in the configuration file (see above).



Running simulation
------------------

Running simualtions requires the following Python scripts

`run_bionet.py` : main python script which calls BioNet to run simulation

`set_cell_params.py`: module setting properties of cells 

`set_syn_params.py`: module setting properties of synapses

`set_weights.py` : modules allowign to set parameter dependent connection weights

The example networks use biophysically-detailed models of individual cells, and require additional NEURON channel mechanisms describing dynamics of ionic channels. To compile these NEURON mechanims go to the subdirectory examples/simulators/bionet/components/mechanisms and run the NEURON command:
::

   nrnivmodl modfiles/

From the directory of the network example you can run a simulation on a single core by executing the main python script with configuration file as a command line argument, as follows:
::

  python run_bionet.py config.json

Or to run in parallel with MPI on $NCORES CPUs:
::

  mpirun -np $NCORES nrniv -mpi -python run_bionet config.json

In either case, the main script will load the configuration file containing paths to files describing the network and will load and simulate the network. 

When simulation is finished, you will see a message “Simulation completed”.

BioNet allows saving simulation output in blocks while simulation is still running, giving users an ability to check and analyze intermediate output. During the run you will see some output reporting on the progress of a simulation as follows:


When simulation completed you will see a message "Simulation completed".

Simulation output
-----------------

The output directory includes:

* spikes.h5 : HDF5 file containg the spikes of the simulated cells.
* cellvars/N.h5 : HDF5 file containing time series recordings of somatic variables  (e.g., somatic voltage, [Ca++]) for cell with node_id=N (there might be multiple such files, up to the number of cells in the model, or none at all, depending on the settings in the simulation config).
* config.json : a copy of configuration for record keeping
* log.txt : run log file including time-stamped information about the progress of a simulation.


Upon completion you may run the script plot_rasters.py to plot spike rasters of external (Figure 2) as well as simulated (Figure 3) cells:
::

  python plot_rasters.py


.. image:: _static/images/ext_inputs_raster.png
   :scale: 70 %

**Figure 2.** Spike rasters of the external input cells: LGN (green) and TW (firebrick).

|

.. image:: _static/images/v1_raster.png
   :scale: 70 %

**Figure 3.** Spike raster of the simulated (V1) cells.



Simulating your network models
------------------------------

To run simulations of your network with BioNet, you will first need to provide a pre-built network in the format understood by BioNet. We recommend using `BMTK's network builder api <builder>`_, but you may also use your own scripts or a third party tool to build a network. As a start we suggest to modify the existing network examples as a quick way of customizing network models and then build your own model following `builder examples tutorial <https://github.com/AllenInstitute/bmtk/tree/develop/examples/builder/bionet_14cells>`_.

When you have your custom model built, you will need to specify in your configuration file the paths to the network, components as well as simulation run parameters.

Just as in the above examples, your run folder should include Python modules: set_cell_params.py, set_syn_params.py, set_weights.py specifying how models of cells, synapses and connection weights are created, as well as a main python script. 

When running different simulations you will rarely need to modify the main Python script running BioNet. Instead, you will commonly need to modify paths to network files or run parameters in the configuration file  to instruct BioNet which model to run and how to run it. Please refer to the `configuration file documentation <./bionet_config.html>`_ for details.


