Neuroscience Gateway, NSG
allows computational neuroscientists to run parallel simulations,
free of charge, on supercomputers using tools like
GENESIS, NEURON, NEST, Brian, and PyNN (descriptions below).
NSG provides a simple web-based interface that makes it quick and easy
to create an account, upload model code, run simulations, and get back
Provides for biophysical modeling of intracellular calcium dynamics.
It allows the inclusion of mobile and/or fixed calcium buffers, as well as
various diffusion barriers. Written in C++. Tested on Linux, SGI, SUN and
One can address questions at the level of molecular interactions and
signal transduction in biological systems, where local concentration
effects and the geometry of interactions are important. Input files are
formatted in NeuroML.
STEPS is a package for exact stochastic simulation of reaction-diffusion
systems in arbitrarily complex 3D geometries. Developed for simulating
detailed models of neuronal signaling pathways in dendrites and around
synapses where spatial gradients and morphology are important. Written in
and controlled with Python.
The biologically oriented user interface allows experimentalists to
create models, define cellular geometry, specify simulations and analyze
the simulation results. The solver is transparent to the average user,
but is accessible to the theorist. SBML import/export.
GENESIS is a general purpose simulation platform which was developed to
support the simulation of neural systems ranging from complex models of
single neurons to simulations of large networks made up of more abstract
neuronal components. XODUS, the graphic front end, requires the X Window
System. PGENESIS (Parallel GENESIS) depends on PVM. The most recent
development efforts can now be followed at
A complete rewrite of GENESIS as a core structure, integrating
independently developed software components. Currently, an
interactive shell, a model container, a fast compartmental solver,
scheduler, graphical browser and a developer package are available
as an alpha release
source code or rpm, deb, and pkg packaged binaries).
Notably, the model container deals with biological entities, and
can import / export NeuroML. Also, Python bindings are directly
available from the new sspy scheduler. This is now the preferred
Python interface and allows easy access to user developed plug-ins.
Analysis and simulation of multiple axonal arbors. Imports Neurolucida
Model Interaction Environment for Neuroscience
MIEN provides a framework for storing, integrating, and interacting with
neuroscience data, including anatomy data, physiology data, abstract
mathematical models, and detailed compartmental models. MIEN is not a
compartmental model simulator, but it provides an interface to the Neuron
simulator for evaluation of compartmental models. Writen in Python.
MOOSE spans the range from single molecules to subcellular networks, from
single cells to neuronal networks, and to still larger systems. It is
backward compatible with GENESIS, and forward compatible with Python and
XML-based model definition standards like SBML and NeuroML.
NEURON is a simulation environment for modeling individual neurons and
networks of neurons. It provides tools for conveniently building,
managing, and using models in a way that is numerically sound and
computationally efficient. It is particularly well-suited to problems
that are closely linked to experimental data, especially those that
involve cells with complex anatomical and biophysical properties. Python
can now be used as alternative command line interpreter, and NeuroML
model descriptions can be imported.
PyDSTool is related to DSTool (see
Differential Equation Solvers
below), but this new simulation, modeling, and analysis package contains
additional toolkits and features for computational neuroscience. It
includes: a model development environment, with templates for
compartmental models; data analysis tools
and built-in tools for continuation / bifurcation analysis.
- Simulation of Neural Networks and Action Potentials
Rapid simulation of single neurons (including ion channels and second
messenger concentrations) and small networks of neurons.
Surf-Hippo allows construction of morphometrically and biophysically
detailed models of single neurons and networks of neurons. Written in
Lisp for both Unix and PC installations. It can accept anatomical data in
Neurolucida, NTS, Rodney Douglas, and Rocky Nevin formats. Built-in
functions also allow user developed anatomical descriptions to be
Biophysical modeling of cells and membranes, as well as connectionist
networks. Phaseplane analysis. Takes advantage of a variety of numerical
There is now a
XPP will run on Windows, Unix/Linux, OS X and now on
A very much
is still available at CMU.
AnimatLab combines biomechanical simulation and biologically realistic
neural networks. One can build the body of an animal, or robot, and place
it in a virtual world with physically accurate interaction with the
environment. For the associated nervous system, the software currently
has support for simple firing rate or ion based leaky integrate and fire
spiking neural models. There are also a number of synapse model types. On
the biomechanics side there is support for a variety of different rigid
body types, including custom meshes that can be made to match skeletal
structures exactly. Hill-based muscle and stretch receptor models allow
production of movements around joints. Motorized joints are also provided
for controlling biomimetic machines. C++ source included.
Brian is a simulator for spiking neural networks. Both integrate-and-fire
models and Hodgkin-Huxley type models can be used. Brian is useful for
models with a few compartments, but not with reconstructed dendritic
trees. Written in Python and supported by the PyNN API, Brian will run on
Windows, Unix/Linux and OS X.
Provides a PyNN-like API. Parameters can be set at the synapse, neuron,
and network level. Supported features include: Izhikevich neurons,
current-based and conductance based synapses, STDP, STP and homeostatic
synaptic scaling. Automated parameter tuning uses evolutionary algorithms.
CSIM is a tool for simulating heterogeneous networks composed of different
classes of model neurons (analog/spiking) and synapses (Static/STDP).
The simulator is written in C++ with a Matlab user interface.
Nengo is designed around the neural engineering framework (NEF). It is
intended for modelling very large networks of neurons, using spiking
point-neuron models. The scripting language used in Nengo is Python.
A simulation system for large networks of biologically realistic (spiking)
neurons. It is best suited for the simulation of large networks of spiking
point-neuron models. The internal dynamics of these models may be
ReMoto is a web-based simulator of the spinal cord and innervated muscles
of the human leg. One may study the output spike trains of a single
neuron (two-compartment model with ionic channels) in response to
different inputs (stochastic or deterministic, synaptic or injected
current) and/or to a change of the dynamic description of its ionic
channels. One may also study the spike trains of whole networks of
spinal neurons as well as the resultant force and electrical activity
of the innervated muscles.
BRAHMS is a modular framework for executing integrated systems built from
component software. Conceptually similar to that of Simulink or Labview,
it links the outputs of some processes into the inputs of others.
Lancet makes it easy to reproducibly specify a parameter space,
run jobs, and collate the output from an external simulator or
analysis tool. The approach is fully general, to allow the researcher
to switch between different software tools and platforms as necessary.
Integrates well with other popular tools such as IPython Notebook and
the pandas data analysis library.
The project provides a standardized software interface for runtime
communication between disparate parallel applications for large-scale
modeling. Built on top of MPI. Demonstrated to work with NEST, Neuron
The neosim project includes a parallel discrete event simulation kernel
for running models of spiking neurons on a cluster of workstations.
Models are specified using NeuroML, and visualised using Java2D.
Software to ease the development of large 3D networks of biologically
realistic neurons for the NEURON, GENESIS, MOOSE, PSICS and PyNN based
simulators. Imports cell morphology files from these simulators as well
as from SWC, Neurolucida and MorphML format files. Cellular mechanisms
can be imported from .mod or .g or ChannelML files. Model generation and
execution also scriptable with Python.
An XML based Markup Language for models in neuroscience. It allows for
specification of neuronal morphology, the distribution of ion channels on
cell membranes, descriptions of the channel mechanisms and of neuronal
connectivity. Current development activity can be followed at
Web based validation of XML files against the various XML Schema
documents which define the NeuroML(MorphML,ChannelML,NetworkML)
specification. Also provides software for translating NeuroML
documents into valid code for use with GENESIS, MOOSE, NEURON
is again mentoring projects in
Google Summer of Code (GSoC)
May - August, 2014.
GSoC is a global program that offers students stipends to write code
for open source projects.
Student application deadline: March 21, 2014.
Simulates ionic currents in real time (up to 50kHz). Can be used to:
(i) artificially insert ion channels into a neuron,
(ii) connect in vitro neurons with simulated synapses, or
(iii) connect simulated neurons to in vitro neurons.
Runs on Linux with the RTLinux kernel extention.
Now replaced by Real-Time eXperiment Interface - RTXI (see below).
Commercial but widely used software for microscope control, 3D neuron
reconstruction, brain mapping and morphometry from either a live camera
image or acquired image stacks. Runs on Windows XP, Vista, 7 in 32 or 64
bit versions. Includes extensive morphological analyses in the companion
product Neurolucida Explorer (included). Hardware support for Zeiss,
Olympus, Nikon and Leica microscopes, motorized stages, filter wheels,
spinning disks, advanced digital cameras, and focus encoders.
Several free tools can be found elsewhere on this page that can directly
use or convert Neurolucida output files.
A modification of DynClamp2 (see above) to include the following features:
(i) a spike generator, (ii) hidden parameter panels, (iii) data displays
for debugging, (iv) saving and loading of parameter settings, (v) spike
timing dependent plasticity, and (vi) experimental automatization and
Java 1.1 application/applet converts Neurolucida morphology files
to GENESIS or NEURON format. It was developed as a morphology
viewer/editor to validate cells contributed to the Duke/Southampton
Cell Morphology Archive. A
is available that will handle remeshing of structures for Genesis.
is a software package for the generation and description of
dendritic morphology. Virtual neurons are created by the stochastic
implementation of neuroanatomical rules. Statistical distributions
of parameters used can be measured from computer files of
reconstructed neurons in several commonly used formats. Generated
neurons can be saved as compartmental files compatible with the
GENESIS and NEURON simulators.
A Generator for realistic Neurons in 3D. NeuGen is made for the
generation of dendritic and axonal morphology of realistic neurons
and neuroal networks in 3D. It directly supports geometry formats
for using the NEURON simulation software.
A plug-in for
that allows one to measure the coordinates
and the diameter of a section of a neuron together with other
information that can be used to reconstruct neuron morphology.
Outputs .swc format files.
A command line program for converting between 3D neuron morphology
formats. Currently 21 formats are supported, including Neurolucida,
SWC, MorphML, NeuroZoom, Eutectics. The viewer is a GUI that
provides interactive views of any format supported by the converter.
Provides tools to automatically reconstruct neuronal branching from
microscopy image stacks, visualize and analyze dendritic and axonal
trees, and quantitatively comparing branching structures between
neurons. Freely distributed but written in Matlab.
V3D is a cross-platform (Mac, Linux, and Windows) tool for
visualizing large-scale (gigabytes, and 64-bit data) 3D image
stacks and various surface data. It includes modules for 3D image
analysis (cell segmentation, neuron tracing, brain registration,
annotation, quantitative measurement and statistics) and data
An open-source analysis toolbox for multiple-neuron recordings and
network simulations (currently works with NEST and PyNN).
Environments can be compared using identical analysis methods. This
allows comparing results across experiments and laboratories as
well as direct comparison of simulation results and
A Matlab toolbox which enables a user to perform manual clustering
on single-electrode, stereotrode, and tetrode recordings taken with
the DataWave and Cheetah recording systems. The toolbox is
free-ware, but you will need Matlab 5.2 or higher to run it. Tested
on Windows and Solaris.
Neo is a package for representing electrophysiology data in Python,
together with support for reading a wide range of neurophysiology
file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon,
Blackrock, Plexon, Tdt, and support for writing to a subset of
these formats plus non-proprietary formats including HDF5.
From the NeuralEnsemble initiative.
neurALC is an open-source cross-platform software for the analysis
of multi-electrode recordings. It also offers analysis functions
like population activity estimation, single electrode/unit PSTH, ISI
& instant firing rate calculation, correlation/autocorrelation,
spectrum, delay, mutual information, recurrence plots, among others.
Visualization of the recordings can be done in 2D and 3D.
Transforms a simple description of the geometry and activity of a
network of neurons and transforms it to a 3D animation.
C++ with the addition of the OpenGL extension of QT. Tested on
Fedora II with QT 3.3.
A modular tool for neuroscience databases. Helmholtz is an open-source
tool for developing customized neuroscience databases, implemented as a
series of components built with Python and the Django web framework. It
provides an abstraction of the underlying database layer, so that any
supported relational database can be used (e.g. MySQL, PostgreSQL,
Oracle or the built-in SQLite).
The NeuroScholar system is a knowledge management system for the
neuroscientific literature, allowing users to build an organized library
of PDF files and then make and manage free-form notes based on the
Users design and construct their own custom GUI screens for data entry,
meta-data annotation, queries, and linkage to analytical tools. XML based.
No longer in development. Replaced by Yogo (see below).
Very good for phaseplane analysis.
for Redhat linux are available.
For current development efforts providing expanded capabilities as a full
modeling package see PyDSTool (within