NetPyNE – Multiscale modeling of brain circuits

Overview

Powerful Features.
‍Gain insights into your
experimental data

NetPyNE is an open-source Python package to facilitate the development, parallel simulation, analysis, and optimization of brain circuit models using the NEURON simulator.

Easy specification

Define models through an intuitive, standardized, declarative Python-based format, officially endorsed by INCF as a standard

Simulation-ready models

Converts high-level specification into NEURON network instance ready for parallel simulation.

Built-in analysis & visualization

Wide range of functions to explore connectivity, voltages/currents, spikes, LFPs, EEG, stats, information measures, ...

Define. Set model properties accross all scales.

Declarative language

Standardized, high-level specification of biological parameters (programmatic or via Graphical Web App)

Multiscale

Include multiple scales from intra- and extra-cellular molecular reaction-diffusion (RxD) to cellular to circuit level.

Cells and populations

Define cell morphologies, biophysics, density, spatial distribution, ...

Connectivity & stimulation

Specify rule-based, complex connectivity and stimulation patterns at the population, cell, and dendritic level

Importing

Use existing cell models defined in HOC, Python or NeuroML

Simulate.
Run parallel simulations.

Efficient implementation

Automated, robust and well-tested backend NEURON implementation prevents coding errors and inefficiencies.

Parallel simulation

Easily set up and run parallel simulations, including distributing cells and gathering data from computing nodes.

Network instance

Generate Python hierarchical structure with NEURON network objects directly from declarative specification.

Analyse. Visualize & explore your network and simulation output.

Connectivity

Connectivity matrix or bar plots showing probability, weights or number of connections

Spiking activity & statistics

Raster plot, histograms, spectrograms, average rate, variability, & synchrony measures.

Neurons 3D explorer

State-of-the-art neuron morphology 3D viewer and 3D shape plot with color-coded variables (e.g number of synapses)

Intrinsic cell properties

Analysis and plotting of membrane voltages, ionic/synaptic currents, conductances, molecular concentrations, etc

Network 2D representation

2D representation of network cell locations and connections

Information transfer

Normalized transfer entropy and spectral Granger Causality

LFP and CSD

Local field potentials (LFP) plots (time-resolved, power spectra and spectrogram), and current source density (CSD) analysis

EEG

Scalp electroencephalogram (EEG) signals based on neuron current dipole moments and head volume conduction model (using LFPykit)

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Parameter exploration/optimization

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Automatically explore or optimize model parameters via grid search or evolutionary algorithms

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Easy specification of parameters and range of values to explore

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Pre-defined, configurable setups to automatically submit jobs in multicore machines (bulletin board) or supercomputers (SLURM or PBS Torque)

Data Sharing

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Save/load high-level specs, network instance, simulation configuration, and simulation results

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Multiple formats supported: JSON, Pickle, Matlab, HDF5

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Export/import to/from NeuroML and SONATA standardized formats for neural models

Explore many models developed by our growing community.

Explore the growing list of NetPyNE models being developed by the user community, and use them as a starting point, to get ideas or to borrow components. These include models of many different brain regions (e.g. cortex, thalamus, hippocampus, cerebellum), disease (e.g. epilepsy, schizophrenia, ischemia), treatments (e.g. ketamine, TMS/tDCS stimulation) and other brain phenomena (e.g. oscillatory coding, sensorimotor learning, memory retrieval).

Open Models Library

Multiscale model of primary motor cortex (M1) circuits

Auditory thalamocortical detailed model

Somatosensory thalamocortical detailed model

Motor, somatosensory and auditory thalamocortical circuit models

CA3 network

Potjans & Diesmann cortical microcircuit model

M1 model based on Izhikevich neurons

Thalamocortical network with epilepsy

Alterations of schizophrenia-associated genes on gamma band oscillations

Single cell (M1 PT) batch parameter exploration example

Pyramidal-tract project (PT) corticospinal cell in M1

Spiking neuronal network model of visual-motor cortex playing a virtual racket-ball game

Spiking neuronal networks performing motor control

A simple sensorimotor network with STDP and RL that learns to drive a simple virtual arm

CA1 microcircuit exhibiting phase-amplitude coupling

Using NeuroML import of a simple network

Computer model of the spinal dorsal horn

Neurorobotics model of Parkinson's Disease

Computational model of the basal ganglia-thalamus-cortex

Model of Parkinson’s Disease tuned to reproduce oscillatory behavior

More models

A graphic showing all integrations to NetPyNE

A graphic showing all standards related to NetPyNE, which includes NeuroML and Sonata

A graphic showing tools linked to NetPyNE, which includes LFPy, HNN, CoreNEURON, The Virtual Brain, SciUnit and NeuronUnit.

A graphic showing platforms linked with NetPyNE, which includes Ebrains, Google Cloud, Open Source Brain and Neuroscience Gateway.

Enjoy interfaces and integration with many tools, platforms and standards.

NetPyNE's growing ecosystem includes interfaces with many tools, e.g., coreNEURON, LFPy, The Virtual Brain and SciUnit; standards in the field, e.g., NeuroML and SONATA; and integration with multiple platforms, e.g., Open Source Brain (OSB), EBRAINS and the Neuroscience Gateway (NSG).

Loved by the neuroscientists around the world

Andreas Neef

Göttingen Center for Neurosciences

It works very well, scales nicely (we tested between 8 and 192 cores) and was in general easy to implement. Thanks for the great tool. I intend to use it in the near future.

Martha Bagnall

Washington University School of Medicine

I'm really excited about your software! I've coded in NEURON and it's a real relief to use a higher-level language.

Mohamed Sherif

Brown University

I have an undergraduate who really likes NetPyne, she said "your code turns into neurons" which I think is outstanding - she doesn't have any computational neuroscience experience before.

Aman Aberra

Duke University

I recently moved the network to NetPyNe to more easily parallelize on our cluster and try out different single-cell models/mechanisms, which has been great.

Parvin Zarei

University of Melbourne

Thank you so much for developing NetPyNE. It made implementing so many things much easier. Such an awesome tool.

Logan Bayer

Sent in the forum

NetPyNE has been incredible to learn and work in!

Andreas Neef

Göttingen Center for Neurosciences

It works very well, scales nicely (we tested between 8 and 192 cores) and was in general easy to implement. Thanks for the great tool. I intend to use it in the near future.

Martha Bagnall

Washington University School of Medicine

I'm really excited about your software! I've coded in NEURON and it's a real relief to use a higher-level language.

Mohamed Sherif

Brown University

I have an undergraduate who really likes NetPyne, she said "your code turns into neurons" which I think is outstanding - she doesn't have any computational neuroscience experience before.

Aman Aberra

Duke University

I recently moved the network to NetPyNe to more easily parallelize on our cluster and try out different single-cell models/mechanisms, which has been great.

Parvin Zarei

University of Melbourne

Thank you so much for developing NetPyNE. It made implementing so many things much easier. Such an awesome tool.

Logan Bayer

Sent in the forum

NetPyNE has been incredible to learn and work in!

Powerful Features.
‍Gain insights into your
experimental data

Define. Set model properties accross all scales.

Simulate.
Run parallel simulations.

Analyse. Visualize & explore your network and simulation output.

Explore many models developed by our growing community.

Enjoy interfaces and integration with many tools, platforms and standards.

Loved by the neuroscientists around the world

Learn more with our resources.

Installation

Documentation

Tutorials

Forum

Get the latest news about NetPyNE directly in your inbox: new features, version releases, courses and events, etc.

Overview

Documentation

Support

Resources

Get the latest news about NetPyNE directly in your inbox: new features, version releases, courses and events, etc.

Overview

Documentation

Support

Resources

Get the latest news about NetPyNE directly in your inbox: new features, version releases, courses and events, etc.

Overview

Documentation

Support

Resources

Powerful Features.‍Gain insights into yourexperimental data

Define. Set model properties accross all scales.

Simulate.Run parallel simulations.

Analyse. Visualize & explore your network and simulation output.

Explore many models developed by our growing community.

Enjoy interfaces and integration with many tools, platforms and standards.

Loved by the neuroscientists around the world

Learn more with our resources.

Installation

Documentation

Tutorials

Forum

Get the latest news about NetPyNE directly in your inbox: new features, version releases, courses and events, etc.

Overview

Documentation

Support

Resources

Learn more, your way.

Step-by-step tutorials

Video tutorials

Get the latest news about NetPyNE directly in your inbox: new features, version releases, courses and events, etc.

Overview

Documentation

Support

Resources

Get the latest news about NetPyNE directly in your inbox: new features, version releases, courses and events, etc.

Overview

Documentation

Support

Resources

Powerful Features.
‍Gain insights into your
experimental data

Simulate.
Run parallel simulations.