COINCIDENCE DETECTION MODELS IN AUDITORY BRAINSTEM

描述(摘自申请者摘要)：该项目的长期目标 研究是对用于双耳声音的符合检测器神经元进行表征 在鸟类和哺乳动物脑干中的定位，使用计算， 生物物理学上准确的，单个神经元的模型。具体研究 目标是：1)建立和表征鸟类脑干核团模型 利用雏鸡的生理数据进行层状(NL)神经元， 能够复制真正的鸡NL神经元的行为和能力； 2)谷仓猫头鹰(Tyto Alba)NL神经元模型的建立和鉴定 复制真正的谷仓猫头鹰NL的行为和优越的能力 神经元，使用谷仓猫头鹰的生理数据(由来自 鸡)；3)修改和推广(1)和(2)的模型以进行检验 哺乳动物脑干内侧上橄榄(MSO)细胞的符合检测 使用哺乳动物的数据。这些模型将被设计来确定和分析 解剖和生理特征对符合检测至关重要。这个 雏鸟和谷仓猫头鹰的基本特征相似，但至关重要 差异将赋予谷仓猫头鹰明显更大的能力。这个 NL和MSO之间的差异更大，但它们的功能 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进行修改 不同之处。研究的设计和方法是基于构建 神经建模环境神经元中的模型(可从耶鲁大学免费获得 大学)，具有电压依赖的电导机制和突触 用C编程语言编写的机构，以求速度。解剖学 描述模型细胞的生理参数取自 文学。每个模型神经元都有多个树突，一个胞体，一个轴突丘，一个 有髓节段和兰维尔结节。的数量、长度和分支 树枝晶是自由参数，可以随温度变化。树枝状和 体节有低电压激活(LVA)和高压激活(LVA) (Hva)K+电导。兰维尔轴突、小丘和结节有棘突 产生/传播电压依赖的离子通道。无锁相， 加入了扩散、去极化抑制。输入向量强度为 链接到神经元的最佳频率(BF)和模型的NL版本 此外，还将高炉与枝晶长度联系在一起。该模型自动生成 生理上有用的统计数据(例如，尖峰率和矢量强度) 数字和图形形式。这项研究的健康相关性来自于 对声音定位的神经机制有了进一步的了解。 了解这些机制将极大地帮助人们寻找助听器。 以及人工耳蜗，这将使用户更好地定位声音 而不是现在的技术。

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.