Debian Science Modeling of neural systems packages

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.

NEURON offers

• "natural syntax", which allows one to specify model properties in familiar idioms
• efficient and painless spatial and temporal discretization
• several different, user-selectable numerical integration methods
• convenient user interface (interpreters + GUI)
• user-extendable library of biophysical mechanisms

Brian es un simulador basado en reloj para redes neuronales de impulsos. Esta diseñado con énfasis en la flexibilidad y extensibilidad, para el rápido desarrollo y refinamiento de modelos neuronales. Modelos de neurona especificados por los sistemas de ecuaciones diferenciales especificados por el usuario, condiciones umbral y condiciones de reinicio (dadas como cadenas de caracteres). Esta enfocado principalmente en redes de modelos de neurona de un solo compartimiento (por ejemplo, de integrar y disparar con fugas o neuronas de tipo Hodgkin-Huxley). Las características incluyen:

• un sistema para especificar cantidades con dimensiones físicas
• integración numérica exacta de ecuaciones diferenciales lineales

• integración de Euler, Runge-Kutta y Euler exponencial para ecuaciones diferenciales no lineales

• conexiones sinápticas con retrasos

• plasticidad a corto plazo y a largo plazo (plasticidad dependiente tiempo de impulso)

• una biblioteca de componentes de modelo estándar, incluyendo ecuaciones de integrar y disparar, sinapsis y corrientes iónicas

• una caja de herramientas para montar automáticamente modelos de neuronas de impulso a grabaciones electrofisiológicas

PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names.

XPPAUT is a tool for solving

• differential equations,
• difference equations,
• delay equations,
• functional equations,
• boundary value problems, and
• stochastic equations.

The code brings together a number of useful algorithms and is extremely portable. All the graphics and interface are written completely in Xlib which explains the somewhat idiosyncratic and primitive widgets interface.

NEST is a simulation system for large networks of biologically realistic point-neurons and neurons with a small number of electrical compartments.

PCSIM is a tool for simulating heterogeneous networks composed of different model neurons and synapses. This simulator is written in C++ with a primary interface to the programming language Python. It is intended to simulate networks containing up to millions of neurons and on the order of billions of synapses. This is achieved by distributing the network over different nodes of a computing cluster by using MPI.

The iLab Neuromorphic Vision C++ Toolkit (iNVT, pronounced ``invent'') is a comprehensive set of C++ classes for the development of neuromorphic models of vision. Neuromorphic models are computational neuroscience algorithms whose architecture and function is closely inspired from biological brains. The iLab Neuromorphic Vision C++ Toolkit comprises not only base classes for images, neurons, and brain areas, but also fully-developed models such as our model of bottom-up visual attention and of Bayesian surprise.

Features at a glance:

• Low-level neural network simulation classes.
• High-level neuromorphic classes.
• Neuromorphic models of visual attention.
• Hardware interfacing
• Parallel processing classes for the simulation of complex models.
• Neuromorphic modeling environment.

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the base and numerical core for large, detailed simulations including Computational Neuroscience and Systems Biology. MOOSE spans the range from single molecules to sub-cellular networks, from single cells to neuronal networks, and to still larger systems. It is backwards-compatible with GENESIS, and forward compatible with Python and XML-based model definition standards like SBML and MorphML. MOOSE is coordinating with the GENESIS-3 project towards the goals of developing educational resources for modeling.