Skip to main content

A Python package to facilitate the development and simulation of biological networks in NEURON.

Project description

# NetPyNE (python package)
## Description
A Python package to facilitate the development and simulation of biological networks in NEURON ([NetPyNE Documentation](http://neurosimlab.org/netpyne/))

NEURON/Python-based modularized framework for network simulations with MPI. Using this modularized structure, users can define different models (including cell types, populations, connectivities, etc.) just by modifying a single parameters file, and easily simulate then in NEURON. Additionally, the framework allows to store a single data file with the following:

1. model specifications (conn rules etc)
2. network instantiation (list of all cells, connections, etc)
3. simulation parameters/configuration (duration, dt, etc)
4. simulation output (spikes, voltage traces etc)

The data file is available in Pickle, JSON and Matlab formats.

Three example model parameters are provided:

1. **HHTut.py** - simple tutorial model with a single Hodgkin-Huxley population and random connectivity
2. **HybridTut.py** - simple tutorial model with a Hodgkin-Huxley and an Izhikevich populations, with random connectivity
3. **M1.py** - mouse M1 model with 14 populations and cortical depth-dependent connectivity.

Additional details of the modelling framework can be found here:

* [NetPyNE Documentation](http://neurosimlab.org/netpyne/)
* [SFN'15 poster](http://neurosimlab.org/salvadord/sfn15-sal-final.pdf)
* [slides](https://drive.google.com/file/d/0B8v-knmZRjhtVl9BOFY2bzlWSWs/view?usp=sharing)


## Setup and execution

Requires NEURON with Python and MPI support.

1. Install package via `pip install netpyne`.

2. Create a model file (eg. model.py) where you import the netpyne package and set the parameters (you can use some of the parameter files incldued in the `examples` folder, eg. `HHTut.py`):

```
import HHTut
from netpyne import init
init.createAndSimulate(
simConfig = HHTut.simConfig,
netParams = HHTut.netParams)
```

3. Type `nrnivmodl mod`. This should create a directory called either i686 or x86_64, depending on your computer's architecture.

4. To run type `python model.py` (or `mpiexec -np [num_proc] nrniv -python -mpi model.py` for parallel simulation).

## Overview of files:

* **examples/**: Folder with examples.

* **doc/**: Folder with documentation source files.

* **netpyne/**: Folder with netpyne package files.

* **netpyne/init.py**: Main executable; calls functions from other modules. Sets what parameter file to use.

* **netpyne/framework.py**: Contains all the model shared variables and modules. It is imported as "s" from all other file, so that any variable or module can be referenced from any file using s.varName

* **netpyne/sim.py**: Simulation control functions (eg. runSim).

* **netpyne/network.py**: Network related functions (eg. createCells)

* **netpyne/cell.py**: contains cell and population classes to create cells based on the parameters.

* **netpyne/analysis.py**: functions to visualize and analyse data

* **netpyne/default.py**: default network and simulation parameters

* **netpyne/utils.py**: utility python methods (eg. to import cell parameters)



For further information please contact: salvadordura@gmail.com

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

netpyne-0.4.0.tar.gz (209.2 kB view details)

Uploaded Source

Built Distribution

netpyne-0.4.0-py2-none-any.whl (36.5 kB view details)

Uploaded Python 2

File details

Details for the file netpyne-0.4.0.tar.gz.

File metadata

  • Download URL: netpyne-0.4.0.tar.gz
  • Upload date:
  • Size: 209.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for netpyne-0.4.0.tar.gz
Algorithm Hash digest
SHA256 2e1ea0f3d84e875d039c53a935da6799087e631bfb95245748edc28da677fad9
MD5 5c1492d8bf866321de050e0b609f1e56
BLAKE2b-256 8d43e95ea02b47ed1fe008b67eceba2c2cf59b9fb5239289a1998599057ff384

See more details on using hashes here.

File details

Details for the file netpyne-0.4.0-py2-none-any.whl.

File metadata

File hashes

Hashes for netpyne-0.4.0-py2-none-any.whl
Algorithm Hash digest
SHA256 5d9b6bbe307843d829615d6d1f02cc629a5df02e94e2dac68490c9e4813e5453
MD5 949208f7aa03e8a03700f1c001c76bc2
BLAKE2b-256 6d3e05bb4dd7b0acfbc251ae333035ced6920a6a2ed33e730cbfc6040965b364

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page