Nonlinear Sciences – Adaptation and Self-Organizing Systems
Scientific paper
2008-10-22
Nonlinear Sciences
Adaptation and Self-Organizing Systems
submitted
Scientific paper
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 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.
Cessac Bruno
Rochel Olivier
Viéville Thierry
No associations
LandOfFree
Introducing numerical bounds to improve event-based neural network simulation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Introducing numerical bounds to improve event-based neural network simulation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Introducing numerical bounds to improve event-based neural network simulation will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-416531