van Elburg Ronald A J, van Ooyen Arjen
Department of Artificial Intelligence, Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, 9700 AB, The Netherlands.
Neural Comput. 2009 Jul;21(7):1913-30. doi: 10.1162/neco.2009.07-08-815.
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.
卡内瓦莱和海因斯提出了一种基于事件的积分方案,用于具有指数衰减兴奋性突触电流和双指数抑制性突触电流的积分发放神经元模型。然而,该积分方案对突触电流的时间常数施加了非生理约束,这限制了其普遍适用性。本文从两个方面解决了这个问题。首先,我们给出了物理学论据,说明了为何可以放宽对时间常数的这些约束。其次,我们给出了一个形式证明,表明哪些约束可以消除。作为形式证明的一部分,我们引入了广义卡内瓦莱 - 海因斯引理,这是一种用于比较双指数函数的新工具,双指数函数在许多级联衰减系统中自然出现,包括受体 - 神经递质解离后接着通道关闭的情况。通过反复应用广义引理,我们解除了大部分对时间常数的原始约束。因此,我们表明,用于积分发放模型的卡内瓦莱 - 海因斯积分方案可用于模拟比之前认为的范围广泛得多的神经元和突触类型。