Introducing numerical bounds to improve event-based neural network simulation
Identifieur interne : 003C66 ( Main/Exploration ); précédent : 003C65; suivant : 003C67Introducing numerical bounds to improve event-based neural network simulation
Auteurs : Bruno Cessac [France] ; Olivier Rochel [France] ; Thierry Viéville [France]Source :
Descripteurs français
- Wicri :
- topic : Simulation.
English descriptors
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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Le document en format XML
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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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Provence-Alpes-Côte d'Azur</li></region><settlement><li>Nice</li></settlement><orgName><li>Université Nice Sophia Antipolis</li></orgName></list><tree><country name="France"><region name="Provence-Alpes-Côte d'Azur"><name sortKey="Cessac, Bruno" sort="Cessac, Bruno" uniqKey="Cessac B" first="Bruno" last="Cessac">Bruno Cessac</name></region><name sortKey="Rochel, Olivier" sort="Rochel, Olivier" uniqKey="Rochel O" first="Olivier" last="Rochel">Olivier Rochel</name><name sortKey="Vieville, Thierry" sort="Vieville, Thierry" uniqKey="Vieville T" first="Thierry" last="Viéville">Thierry Viéville</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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