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FENS Forum 2010 - Amsterdam

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Poster sessions run from 09:30 to 13:15 in the morning and from 13:30 to 17:30 in the afternoon.
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First author: Raikov, Ivan (poster)

Poster board G26 - Sun 04/07/2010, 13:30 - Hall 1
Session 059 - Neurotechniques 2
Abstract n° 059.26
Publication ref.: FENS Abstr., vol.5, 059.26, 2010

Authors Raikov I. (1, 2) & De Schutter E. (1, 2)
Addresses (1) Okinawa Institute of Science and Technology, Okinawa, Japan; (2) University of Antwerp, Antwerp, Belgium
Title A modeling language for large-scale neural network description
Text We propose a modeling language for simulator-independent description of large networks of integrate-and-fire neurons. We have first identified a minimal set of mathematical structures that are necessary to express key concepts of integrate-and-fire modeling. We have then encapsulated this mathematical base in a flexible, extensible computer language that can be automatically translated to the input format required by particular simulators, such as NEST or NEURON. The essential concepts of integrate-and-fire network modeling are: 1) spiking neurons, 2) synapses, 3) populations of neurons, 4) connectivity patterns across populations of neurons. Accordingly, the mathematical abstractions we propose are aimed at representing these concepts.
First, we propose a flexible block diagram notation for describing spiking dynamics. The notation represents continuous and discrete variables, their evolution according to a set of rules such as a system of ordinary differential equations, and the conditions that induce a regime change, such as the transition from sub-threshold mode to spiking and refractory modes. The notation we have developed is an explicit formalization of event handling and is an important step in ensuring model simulation consistency. Furthermore, we propose a grid abstraction that can describe neuronal populations of diverse topologies. We address the requirements of sensory cortex modeling by including in the language a facility for describing multidimensional linear or logarithmic spaces that enable the modeling of neuronal topologies based on physical location and feature maps. Finally, we adopt a graph theoretic approach to describing the connectivity patterns between neuronal populations. An abstraction based on graph rewriting rules allows for the expression of many structural and functional properties of neuronal networks.
Theme G - Techniques, history and teaching
Neurotechniques

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