Neuralsyns:A Novel Tool for Simulating Large Neuronal Networks
With Complex Architectures
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1
Universidade do Porto, Centro de Matematica, Portugal
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2
Instituto de Biologia Molecular e Celular-IBMC, Portugal
Even the simplest operations in the brain involve the interaction and cooperation between several populations of neurons with distinct functional roles. Said that, it is crucial to remember that, while necessary, knowledge of the dynamics of each isolated functional component is not a sufficient condition to understand a functional system as a whole. To fully understand how specific computational operations can be achieved (e.g. memory formation in the hippocampus, nociceptive information integration in the dorsal horn) detailed and biophysically realistic representations of the whole system, including the interactions between the parts, have to be build. Only these holistic representations provide a robust ground for model analysis and hypothesis testing.
Here we present NeuralSyns, a simulation environment which facilitates this process by being capable of producing and analyzing large heterogeneous networks of neurons with complex architectures. As opposed to other well known simulation environments (e.g. Neuron, Genesis or NEST), the learning curve for NeuralSyns is very steep as no programming language is required to be learned. Neurons and synapses in NeuralSyns can be modeled using a vast library of dynamics, and new user-defined dynamics can be added. The simulation engine is written in C and makes use of the GNU Scientific Library. For numerical precision, NeuralSyns uses a second order Runge-Kutta method with a linear interpolant to find spike times and recalibrate post-spike potentials. The engine is capable of handling a vast number of properties including synaptic plasticity, stochastic activity, detailed 3D architectures, topographic connectivities, among others. NeuralSyns is able to analyse network models with millions of neurons and billions of synapses. One of NeuralSyns' powerful features is its ability to produce detailed 3D graphical representations of the model's architecture and dynamics. This feature uses OpenGL libraries and makes the simulation environment a valuable tool not only for research but also for class demonstration purposes. NeuralSyns is open-software and can be downloaded for free from http://sourceforge.net/projects/neuralsyns
Supported by FCT Grant - PTDC/SAU-NEU/68929/2006. The author was supported by the Centro de Matemática da Universidade do Porto, www.fc.up.pt/cmup, financed by FCT through the programs POCTI and POSI, with Portuguese and European Community structural funds.
Conference:
11th Meeting of the Portuguese Society for Neuroscience, Braga, Portugal, 4 Jun - 6 Jun, 2009.
Presentation Type:
Poster Presentation
Citation:
De-Castro-Aguiar
P
(2009). Neuralsyns:A Novel Tool for Simulating Large Neuronal Networks
With Complex Architectures.
Front. Neurosci.
Conference Abstract:
11th Meeting of the Portuguese Society for Neuroscience.
doi: 10.3389/conf.neuro.01.2009.11.045
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Received:
06 Aug 2009;
Published Online:
06 Aug 2009.
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Correspondence:
Paulo De-Castro-Aguiar, Universidade do Porto, Centro de Matematica, Porto, Portugal, pauloaguiar@ineb.up.pt