Grupo de Neurocomputacion Biologica, Departamento de Ingeniería Informatica, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain.
PLoS One. 2010 Dec 28;5(12):e15023. doi: 10.1371/journal.pone.0015023.
Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for transient memory, or context-dependent information processing: depending upon the current context that the neuron "sees" through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE) as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF) neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF) neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and functional connectivity of different neuronal types.
神经元对传入的刺激会根据其先前的刺激历史做出不同的反应。这种特性可以被认为是瞬时记忆或上下文相关信息处理的单细胞基础：取决于神经元“看到”的当前上下文，即过去瞬间网络中影响它的子集，相同的突触事件可以引发突触后放电或仅仅是亚阈去极化。我们提出了一种历史相关兴奋性（HDE）的正式定义，作为任何时刻发射倾向的度量，将亚阈历史相关动力学与尖峰生成联系起来。这个定义允许对不同单神经元动力学和输入统计数据的固有记忆进行定量评估。我们通过考虑两种一般动力学机制来说明 HDE 的概念：积分和点火（IF）神经元的被动行为，以及具有亚阈阻尼振荡的广义积分和点火（GIF）神经元的感应行为。这个框架允许我们描述不同模型神经元对传入刺激的详细时间结构的敏感性。虽然具有内在振荡的神经元可以同样好地区分具有相同或不同频率的输入序列，但被动神经元可以更好地区分具有不同频率的输入。这表明被动神经元更适合基于速率的计算，而具有亚阈振荡的神经元在时间编码方案中更有利。我们还研究了作为输入统计函数的内在特性对单细胞处理的影响，并表明内在振荡可以提高高输入速率下的辨别敏感性。最后，我们讨论了如何识别这些细胞特异性辨别特性可以进一步理解神经元网络计算及其与不同神经元类型的分布和功能连接的关系。
