COINCIDENCE DETECTION MODELS IN AUDITORY BRAINSTEM

描述（来自申请人摘要）：本研究的长期目标 一项研究是描述用于双耳声音的重合检测神经元 在鸟类和哺乳动物脑干中的定位，使用计算， 生物药理学上精确的单个神经元模型。具体研究 目的是：1）建立和表征鸟类脑干核团的模型 使用来自小鸡（鸡）的生理数据的层流（NL）神经元， 能够再现真实的鸡NL神经元的行为和能力; 2)建立和表征仓鸮（Tyto alba）NL神经元的模型， 重现真实的仓鸮NL的行为和上级能力 神经元，使用仓鸮的生理数据（由来自 3）修改和推广（1）和（2）的模型来检验 在哺乳动物脑干内侧上级橄榄（MSO）细胞中的重合检测， 使用哺乳动物的数据。这些模型将被设计用于确定和分析 解剖学和生理学特征对重合检测至关重要。的 雏鸟和仓鸮的基本特征相似， 差异将赋予仓鸮显着更大的能力。的 NL和MSO之间的差异甚至更大，但它们的功能是 符合探测器足够相似，可以建立MSO模型 类似于NL模型，根据解剖学和生理学修改 差异研究设计和方法是基于构建 在神经建模环境NEURON（可从耶鲁大学免费获得）中建模 电压依赖性传导机制和突触 使用编程语言C编写的机制，以提高速度。解剖 并且描述模型细胞的生理参数取自 文学每个模型神经元具有多个树突、一个索马、一个轴突丘、一个轴突突起和一个轴突突起。 有髓节和朗维尔结。的数目、长度和分枝 树突是自由参数，并且可以在色调上变化。树突状细胞和 体细胞切片具有低电压激活（LVA）和高电压激活 (HVA)K+电导。轴突丘和朗维尔结有棘突 产生/传播电压依赖性离子通道。非锁相， 掺入了弥散的去极化抑制。输入矢量强度为 与神经元的最佳频率（BF）相关，以及模型的NL版本 BF也与枝晶长度有关。模型自动生成 生理上有用的统计数据（例如，尖峰频率和矢量强度）， 数字和图形形式。这项研究的健康相关性 从对声音定位的神经机制的理解中。 了解这些机制将大大有助于寻求助听器 以及人工耳蜗，可以让使用者更好地定位声音 比目前的技术。

DESCRIPTION (from applicant's abstract): The long-term objective of this research is to characterize coincidence-detector neurons used for binaural sound localization in the avian and mammalian brainstem, using computational, biophysically accurate, models of individual neurons. The specific research goals are to: 1) build and characterize models of avian brainstem Nucleus Laminaris (NL) neurons using physiological data from chicks (Gallus domesticus), capable of reproducing the behavior and capabilities of real chick NL neurons; 2) build and characterize models of NL neurons in barn owls (Tyto alba) capable of reproducing the behavior and superior capabilities of real barn owl NL neurons, using physiological data from barn owls (supplemented by data from chicks); 3) modify and generalize the models of (1) and (2) to examine coincidence detection in mammalian brainstem Medial Superior Olive (MSO) cells, using mammalian data. The models will be designed to determine and analyze the anatomical and physiological features crucial to coincidence detection. The fundamental features are similar in chicks and barn owls, but crucial differences will give the barn owl its significantly greater abilities. The differences between NL and MSO are even larger, but their functions as coincidence detectors are sufficiently similar to permit MSO models to be built analogously to the NL models, modified according to anatomical and physiological differences. The research design and methods are based on constructing the models in the neural modeling environment NEURON (freely available from Yale University), with voltage-dependent conductance mechanisms and synaptic mechanisms written using the programming language C, for speed. The anatomical and physiological parameters describing the model cells are taken from the literature. Each model neuron has multiple dendrites, a soma, an axon hillock, a myelinated segment, and a node of Ranvier. The number, length, and branching of dendrites are free parameters and may vary tonotopically. The dendritic and somatic sections have low voltage activated (LVA) and high voltage activated (HVA) K+ conductances. The axon hillock and node of Ranvier have spike generating/propagating voltage-dependent ion channels. Non-phase-locked, diffuse, depolarizing inhibition is incorporated. Input vector strength is linked to the best frequency (BF) of the neuron, and NL versions of the model also link BF with dendritic length. The model automatically generates physiologically useful statistics (e.g. spike rate and vector strength) in numerical and graphical forms. The health relatedness of this research comes from the gained understanding of the neural mechanisms of sound localization. Understanding those mechanisms will substantially aid the quest for hearing aids and cochlear implants that would allow their users to localize sounds far better than current technology.