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Contextual Spike-Timing Codes in Sensory Processing

感觉处理中的上下文尖峰计时代码

基本信息

批准号：
7072822
负责人：
Gregory Arthur Clark
金额：
$ 19.71万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-07-01 至 2009-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7072822
关键词：
Hermissenda behavioral /social science research tag computational neuroscience computer simulation learning light intensity neural information processing neural transmission sensory discrimination sensory signal detection visual feedback visual perception visual photoreceptor

项目摘要

DESCRIPTION (provided by applicant): We propose a series of coordinated computational and electrophysiological studies to identify, analyze, and implement contextual spike-timing codes in the encoding of light intensity, using a well-understood, simple visual system (the Hermissenda eye) that permits detailed cellular mechanistic analyses and biologically realistic, Hodgkin- Huxley level simulations. We further propose to examine mechanisms by which neural "noise" paradoxically improves, rather than degrades, light intensity encoding; and the interactions of contextual spike-timing codes with learning-related synaptic plasticity. Implementation experiments to restore network function by explicitly introducing contextual spike-timing relationships may provide powerful, novel evidence regarding the functional consequences of these codes. Specific Aim 1: Role of Network Architecture. Pilot simulation and physiological data indicate that network interactions (feed-forward, feedback, and lateral inhibition) alter contextual spike-timing relationships and may contribute substantially to improved information processing in the network, relative to single cells. We will examine the existence, origins, and consequences of these codes in both the simulated and biological eye. Specific Aim 2: Encoding of Light Intensity. Preliminary evidence indicates that an increase in light intensity produces an increase in both the rate and synchrony of type A photoreceptor spiking, suggesting multiple encoding schemes with potentially complementary roles. Further, the addition of synaptic and ionic neural "noise" improves both rate and synchrony encoding of light intensity. We propose to quantify and compare rate and contextual spike-timing codes across light intensities; examine how their performance is affected by the presence of neural noise; and implement relevant algorithms into a neural interface (or simulated interface) to determine whether they improve control of the system and light intensity encoding. Specific Aim 3: Contextual Spike-Timing Codes and Learning. Preliminary data indicate that synaptic facilitation at B-to-A connections alters the timing relationships between these photoreceptors, and that noise improves rather than degrades the reliability of type A cell responses to enhanced synaptic input. We propose to examine the role of learning-related synaptic plasticity in contextual spike timing encoding, including its effects on light intensity coding and mechanisms of noise-induced enhancements. Taken together, these studies will elucidate neurobiological strategies for information processing, and identify efficient bio-based solutions that may be advantageously incorporated into artificial intelligence systems, robotic systems, or clinical neuroprostheses.

描述（由申请人提供）：我们提出了一系列协调的计算和电生理学研究，以识别、分析和实现光强度编码中的上下文尖峰定时代码，使用易于理解的简单视觉系统（Hermissenda眼），其允许详细的细胞机制分析和生物学上真实的Hodgkin-Huxley水平模拟。我们还建议研究机制，神经“噪音”矛盾地提高，而不是降低，光强度编码;和上下文尖峰定时代码与学习相关的突触可塑性的相互作用。通过明确引入上下文尖峰定时关系来恢复网络功能的实施实验可以提供关于这些代码的功能后果的强有力的、新颖的证据。具体目标1：网络架构的作用。飞行员模拟和生理数据表明，网络相互作用（前馈，反馈和侧抑制）改变上下文尖峰定时关系，并可能大大有助于改善网络中的信息处理，相对于单细胞。我们将在模拟眼和生物眼中研究这些代码的存在、起源和后果。具体目标2：光强度编码。初步证据表明，增加光强度产生的A型光感受器尖峰的速率和同步性的增加，这表明多个编码方案具有潜在的互补作用。此外，突触和离子神经“噪声”的添加改善了光强度的速率和同步编码。我们建议量化和比较不同光强度下的速率和上下文尖峰定时代码;研究它们的性能如何受到神经噪声的影响;并将相关算法实现到神经接口（或模拟接口）中，以确定它们是否改善了系统的控制和光强度编码。具体目标3：上下文尖峰定时代码和学习。初步数据表明，在B到A连接的突触促进改变了这些光感受器之间的时序关系，噪声提高，而不是降低A型细胞响应增强突触输入的可靠性。我们建议研究学习相关的突触可塑性在上下文锋电位定时编码中的作用，包括其对光强度编码的影响和噪声诱导增强的机制。总之，这些研究将阐明信息处理的神经生物学策略，并确定有效的基于生物的解决方案，这些解决方案可以有利地纳入人工智能系统，机器人系统或临床神经假体。