Tutorial 1: Steady-State Current Injection in a Single Cell

Overview

This tutorial demonstrates the simplest example of applying steady-state current injection to a single biophysical neuron.

See also

Corresponding BMTK tutorial: Tutorial: Single Cell Simulation with Current Injection

Changes from bionet to bionetlite

Only the import statement needs to be changed. All other code remains exactly the same.

Before (BMTK):

from bmtk.builder.networks import NetworkBuilder

After (bionetlite):

from bionetlite import NeuliteBuilder as NetworkBuilder

Code Example

from bionetlite import NeuliteBuilder as NetworkBuilder

# Create network
net = NetworkBuilder('mcortex')

# Add a single biophysical neuron
net.add_nodes(
    N=1,
    pop_name='Scnn1a',
    model_type='biophysical',
    model_template='ctdb:Biophys1.hoc',
    dynamics_params='472363762_fit.json',
    morphology='Scnn1a_473845048_m.swc'
)

# Build the network
net.build()

# Save to files
net.save_nodes(output_dir='sim_ch01/network')

Generated Files

The following files are generated in this simulation:

<network_name>_population.csv

A file containing population information that defines the biophysical properties of a single neuron.

id,pop_name,model_type,morphology,dynamics_params,...
0,Scnn1a,biophysical,Scnn1a_473845048_m.swc,472363762_fit.json,...

<src>_<trg>_connection.csv

A file containing synaptic connection information. Since no synaptic connections are defined in this tutorial, the file is empty (header only).

Note

A connection file is required to run the Neulite kernel. If no connections exist, an empty file is generated.

Processed SWC File

A preprocessed morphology file for Neulite is generated.

This file is processed according to the Perisomatic model and the following modifications are made:

  • Replace actual axon morphology with a simplified artificial axon

  • Convert to zero-based indexing

  • Sorting by depth-first search

Note

For details on the Perisomatic model, see Specifications and Limitations.

Files Generated by Neulite

bionetlite generates files in the following directory:

  • If base_dir is set in the script: {base_dir}_nl/

  • If base_dir is not set: neulite/

Directory Structure

sim_ch01_nl/
├── mcortex_population.csv
├── mcortex_mcortex_connection.csv
├── kernel/
│   └── config.h
└── data/
    ├── Scnn1a_473845048_m.swc
    └── 472363762_fit.csv

mcortex_population.csv

A population file for Neulite. The column structure is as follows:

#n_cell,n_comp,name,swc_file,ion_file
1,3682,default_100_100,data/Scnn1a_473845048_m.swc,data/472363762_fit.csv

  • #n_cell: Number of cells

  • n_comp: Number of compartments

  • name: Population name

  • swc_file: Path to SWC morphology file

  • ion_file: Path to ion channel parameter file

mcortex_mcortex_connection.csv

A connection file for Neulite. Since there are no connections in this tutorial, a file with only the header is generated:

#pre nid,post nid,post cid,weight,tau_decay,tau_rise,erev,delay,e/i

kernel/config.h

A compilation-time configuration file for the Neulite kernel. It is automatically generated from BMTK’s config.json:

// Automatically generated by bionetlite

#pragma once

// Simulation parameters
#define TSTOP ( 2000.0 )
#define DT ( 0.1 )
#define INV_DT ( ( int ) ( 1.0 / ( DT ) ) )

// Neuron parameters
#define SPIKE_THRESHOLD ( -15.0 )
#define ALLACTIVE ( 0 )

// Current injection parameters
#define I_AMP ( 0.12 )
#define I_DELAY ( 500.0 )
#define I_DURATION ( 1000.0 )

This file contains the following parameters:

  • TSTOP: Simulation time (ms)

  • DT: Time step (ms)

  • INV_DT: Number of steps per millisecond

  • SPIKE_THRESHOLD: Spike detection threshold (mV)

  • I_AMP: Amplitude of current injection (nA)

  • I_DELAY: Current injection start time (ms)

  • I_DURATION: Duration of current injection (ms)

Note

Since config.h is used when building the Neulite kernel, recompiling the kernel is necessary if you modify this file.

Building and Running the Neulite Kernel

Step 1: Deploy Kernel Sources

Copy the contents of Neulite’s kernel directory to the kernel directory generated by bionetlite. Exclude config.h as it has already been generated by bionetlite:

rsync -av --exclude='config.h' /path/to/neulite/kernel/ sim_ch01_nl/kernel/

Step 2: Build the Kernel

Navigate to the kernel directory and build. config.h has already been generated by bionetlite:

cd sim_ch01_nl/kernel
make

Step 3: Place the Binary

Copy the built binary file nl to the parent directory:

cp nl ../

Step 4: Run the Simulation

Navigate to the directory generated by bionetlite and run the simulation:

cd ..
./nl mcortex_population.csv mcortex_mcortex_connection.csv

Simulation Result Files

The following text files are generated after running the Neulite kernel:

s.dat - Spike Time Data

Space-delimited text format recording one spike event per line:

Time(ms) NeuronID
125.3 0
156.8 0
189.2 0

  • Column 1: Time at which spike occurred (ms)

  • Column 2: ID of the neuron that generated the spike

v.dat - Membrane Potential Data

Space-delimited text format recording membrane potential of all neurons per line:

Time(ms) Neuron0_Vm Neuron1_Vm ...
0.0 -65.0 -65.0
0.1 -64.8 -64.9
0.2 -64.6 -64.8

  • Column 1: Time (ms)

  • Column 2 onwards: Soma membrane potential of each neuron (mV)

Note

These files are in text format (ASCII), so they can be easily loaded with Python’s numpy or pandas.

Comparison of Execution Results with BMTK

Comparison of BMTK and Neulite execution results (dt=0.025)

Comparison plot of execution results between BMTK and Neulite at dt=0.025.

Next Steps

Note

Complete code examples and data files for this tutorial are available in the examples directory of the repository.