NONLINEAR DYNAMICS OF NEURONAL TEMPORAL PROCESSING

描述（改编自申请人的摘要）：据信，一些 神经计算涉及细胞和电路特性， 基于动作电位的精确定时的编码和解码。声音 听觉系统中的定位提供了一个令人信服的例子。它作为 本研究的案例研究旨在更定性地描述 与精确的时间处理相关的细胞特性。 听觉脑干中的许多细胞有助于系统的能力， 检测耳间信号的一致性。这些神经元有独特的放电 特性.当一个稳定的刺激出现时，它们只激发一次， 刺激开始，而许多其他类型的神经元将继续发射，直到 刺激被关闭。这种相位特性被认为是至关重要的， 精确的时间处理相反，假设紧张性细胞较少 能够跟踪快速变化的信号。生物物理学基础，一个特殊的 钾电流，IK-LT似乎是脑干相位的基础 神经元 这个项目将以系统的方式解决时间处理如何 当神经元从相位性转变为紧张性时，神经元的能力发生变化 当细胞处于时相性时， 模式是否能更好地跟踪随时间变化的信号，或者它的性能是否更好 符合检测，比当它是在紧张模式？这项研究将联合收割机 实验和理论方法。实验涉及 在体外刺激单个神经元时的电记录 具有周期性和其它时变信号，包括随机分量。从 在理论方面，将开发基于生物药理学的数学模型 模拟神经元，包括IK-LT的术语。 应用于计算机和细胞模型，以评估可靠性和精度 的加工。此外，非线性动力系统的概念将 应用于揭示和理解潜在的数学结构 这种理解将使我们能够概括的意义， 对其他神经系统的相位性，其中机制可能不涉及IK-LT 一个相关的子项目是开发计算模型， 实验上被视为耳间相位（或振幅或 频率）是动态变化的。更深入地了解这些令人惊讶的 影响，如在听觉中脑中所见，应该有助于发展一种 声源的运动如何在大脑中分析的理论。

DESCRIPTION (Adapted from Applicant's abstract): It is believed that some neural computations involve cellular and circuit properties that enable encoding and decoding based on precise timing of action potentials. Sound localization in the auditory system offers a compelling example. It serves as the case study for this research that seeks a more qualitative characterization of cellular properties that correlate with precise temporal processing. Many cells in the auditory brain stem contribute to the system's ability to detect coincidence of interaural signals. These neurons have distinctive firing properties. When a steady stimulus is presented they fire only once, at stimulus onset, while neurons of many other types will continue to fire until the stimulus is turned off. This property of phasicness is believed crucial for precise temporal processing. In contrast, tonic cells are assumed to be less capable of tracking rapidly changing signals. The biophysical basis, a special potassium current, IK-LT appears to underlie phasicness in the brain stem neurons. This project will address in a systematic way how the temporal processing ability of a neuron changes as the neuron is transformed from phasic to tonic mode, say by gradually adjusting the strength of IK-LT When a cell is in phasic mode does it track a time-varying signal better, or does it perform better coincidence detection, than when it is in tonic mode? The research will combine both experimental and theoretical approaches. The experiments involve electrical recording from individual neurons in vitro while stimulating them with periodic and other time-varying signals, including random components. From the theoretical side, biophysically-based mathematical models will be developed that mimic the neurons, including a term for IK-LT Various measures will be applied to the computer and cellular models to assess reliability and precision of processing. In addition, concepts from nonlinear dynamical systems will be applied in order to reveal and understand the underlying mathematical structure This understanding will enable us to generalize about the significance of phasicness to other neural systems where the mechanism might not involve IK-LT A related subproject is to develop computational models that will help explain the dynamic effects seen experimentally as interaural phase (or amplitude or frequency) is varied dynamically. A deeper understanding of these surprising effects, as seen in the auditory mid-brain, should contribute to developing a theory for how motion of sound sources are analyzed in the brain.