Temporal Processing And Short- And Long-Term Plasticity

时间处理以及短期和长期可塑性

• 批准号: 9983122
• 负责人: Dean Buonomano
• 金额: $ 18.23万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-15 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9983122&HistoricalAwards=false
• 关键词: Temporal; Processing; Short; Long; Term

PI: BuonomanoAbstract Information contained in the temporal patterns of neuronal activity is fundamental to most forms of sensory processing, perhaps most notably speech. How is temporal information decoded by the nervous system? What are the neural mechanisms that permit neurons to develop selective responses to temporal patterns on the time scale of tens to hundreds of milliseconds? The hypothesis guiding the current proposal is that local cortical networks are intrinsically capable of decoding temporal information. Specifically, that short-term plasticity produces time-dependent changes in the balance of opposing excitatory and inhibitory events, and that these time-dependent changes in network state allow neurons to respond differentially to the distinct temporal features of stimuli. Neuronal responses to time-varying stimuli are determined not only by the strength of their excitatory inputs, but by a balance of excitatory and inhibitory inputs, each of which is modulated by short-term forms of plasticity. The projects described here are aimed at understanding how multiple synaptic and cellular mechanisms interact, and the learning rules that govern the long-term plasticity of each process, including short-term plasticity itself. Together, these studies should contribute to the understanding of how the brain times events, as well as to understanding cognitive deficits that may involve temporal processing (such as some forms of dyslexia), and to the generation of artificial systems capable of complex pattern recognition.

主要研究者：Buonomano摘要神经元活动的时间模式中包含的信息是大多数感觉处理形式的基础，也许最明显的是语音。神经系统是如何解码时间信息的？是什么神经机制允许神经元对几十到几百毫秒的时间尺度上的时间模式产生选择性反应？ 指导当前提案的假设是，局部皮质网络本质上能够解码时间信息。 具体来说，短期可塑性产生时间依赖性的变化，在对立的兴奋性和抑制性事件的平衡，这些时间依赖性的网络状态的变化允许神经元不同的时间特征的刺激作出反应。神经元对时变刺激的反应不仅取决于其兴奋性输入的强度，而且还取决于兴奋性和抑制性输入的平衡，其中每一种输入都受到短期形式的可塑性的调制。 这里描述的项目旨在了解多种突触和细胞机制如何相互作用，以及控制每个过程的长期可塑性的学习规则，包括短期可塑性本身。总之，这些研究应该有助于理解大脑如何对事件进行计时，以及理解可能涉及时间处理的认知缺陷（如某些形式的阅读障碍），并产生能够识别复杂模式的人工系统。