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Introducing numerical bounds to improve event-based neural network simulation

Bruno Cessac 1, 2 Olivier Rochel 2, 3 Thierry Viéville 2, 3 
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS-PSL - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis (1965 - 2019), CNRS - Centre National de la Recherche Scientifique : UMR8548
3 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Although the spike-trains in neural networks are mainly constrained by the neural dynamics itself, global temporal constraints (refractoriness, time precision, propagation delays, ..) are also to be taken into account. These constraints are revisited in this paper in order to use them in event-based simulation paradigms. We first review these constraints, and discuss their consequences at the simulation level, showing how event-based simulation of time-constrained networks can be simplified in this context: the underlying data-structures are strongly simplified, while event-based and clock-based mechanisms can be easily mixed. These ideas are applied to punctual conductance-based generalized integrate-and-fire neural networks simulation, while spike-response model (SRM) simulations are also revisited within this framework. As an outcome, a fast minimal complementary alternative with respect to existing simulation event-based methods, with the possibility to simulate interesting neuron models is implemented and experimented.
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Submitted on : Friday, May 29, 2009 - 1:41:42 PM
Last modification on : Wednesday, October 26, 2022 - 8:16:36 AM
Long-term archiving on: : Thursday, June 10, 2010 - 8:16:27 PM


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  • HAL Id : inria-00382534, version 1


Bruno Cessac, Olivier Rochel, Thierry Viéville. Introducing numerical bounds to improve event-based neural network simulation. [Research Report] RR-6924, INRIA. 2009, pp.31. ⟨inria-00382534⟩



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