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Neural Comput. 2009 Jul;21(7):1913-30. doi: 10.1162/neco.2009.07-08-815.

Generalization of the event-based Carnevale-Hines integration scheme for integrate-and-fire models.

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Department of Artificial Intelligence, Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, 9700 AB, The Netherlands.


An event-based integration scheme for an integrate-and-fire neuron model with exponentially decaying excitatory synaptic currents and double exponential inhibitory synaptic currents has been introduced by Carnevale and Hines. However, the integration scheme imposes nonphysiological constraints on the time constants of the synaptic currents, which hamper its general applicability. This letter addresses this problem in two ways. First, we provide physical arguments demonstrating why these constraints on the time constants can be relaxed. Second, we give a formal proof showing which constraints can be abolished. As part of our formal proof, we introduce the generalized Carnevale-Hines lemma, a new tool for comparing double exponentials as they naturally occur in many cascaded decay systems, including receptor-neurotransmitter dissociation followed by channel closing. Through repeated application of the generalized lemma, we lift most of the original constraints on the time constants. Thus, we show that the Carnevale-Hines integration scheme for the integrate-and-fire model can be employed for simulating a much wider range of neuron and synapse types than was previously thought.

[Indexed for MEDLINE]

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