XNBC: a simulation tool. Application to the study of neural coding using hybrid networks

doi:10.1016/0303-2647(96)01648-6

Biosystems

Volume 40, Issues 1–2, 1997, Pages 211-218

https://doi.org/10.1016/0303-2647(96)01648-6 Get rights and content

Abstract

XNBC is a software package for simulating biological neural networks. Two neuron models are available, a leaky integrator model and an ion-conductance model. Inputs to the simulated neurons can be provided by experimental data stored in files, allowing the creation of ‘hybrid’ networks. Graphic tools are used to describe the modeled neurons as well as the network. Neuron and network parameters can be modified during the simulation, to mimic electrical stimulations and drugs action. The temporal evolution of the network and of selected neurons can be visualized. A point process, frequency or dynamic analysis of the simulator output can be performed. The successive stages of the creation of a hybrid network are explained.

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XNBC V9 is a software intended to simulate biological neural networks. Two types of neuron models are available: phenomenological, and ion-conductance based with several receptor types. Inputs to simulated neurons may come from experimental data stored in files, to make “hybrid” networks. Neurons and networks are described using graphic tools. Neurons can be modified, stimulated or receive drugs during the simulation. The temporal evolution of the network and of individual neurons can be visualized. The software includes tools for the graphic representation of point process, frequency and dynamic analyses tools. XNBC is open source (GPL) and available on Internet.
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We have developed a distributed software package which allows neurophysiologists to have at hand a set of network and graphic tools in order to perform the analysis of data collected from the electrical activity of neurons, studied in the experimental laboratory. This system is built around three major components. First, the experimental data are processed in order to select specific data files from the networked computer and display the spike trains as raster dots. The second part selects one type of analysis (e.g., cross-correlation, out of a set of possible choices) after configuration of some specific parameters. Finally, the third component deals with the results of data analyses, which are selected and displayed on a multi-parameter graphic “ring binder.” We have used the Java programming language to implement this application. This recent object-oriented language offers platform-independent software tools including graphic package and standard network protocols.
Effects of transmission delays and noise in recurrent excitatory neural networks
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XNBC: a simulation tool. Application to the study of neural coding using hybrid networks

Abstract

TINS

TINS

Neuroscience

Neuroscience

Neuroscience

Neural Networks

A minimal biophysical model for an excitable and oscillatory neuron

Biol. Cybern.

The hypothalamus of the cat

The Book of Genesis: Exploring realistic neural models with the GEneral NEural SImulation System