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Temporal Decoding by Phase-Locked Loops: Unique Features of Circuit-Level Implementations and Their Significance for Vibrissal Information Processing

机译:锁相环的时间解码:电路级实现的独特功能及其对振动信息处理的意义

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摘要

Rhythmic active touch, such as whisking, evokes a periodic reference spike train along which the timing of a novel stimulus, induced, for example, when the whiskers hit an external object, can be interpreted. Previous work supports the hypothesis that the whisking-induced spike train entrains a neural implementation of a phase-locked loop (NPLL) in the vibrissal system. Here we extend this work and explore how the entrained NPLL decodes the delay of the novel, contact-induced stimulus and facilitates object localization. We consider two implementations of NPLLs, which are based on a single neuron or a neural circuit, respectively, and evaluate the resulting temporal decoding capabilities. Depending on the structure of the NPLL, it can lock in either a phase- or co-phase-sensitive mode, which is sensitive to the timing of the input with respect to the beginning of either the current or the next cycle, respectively. The co-phase-sensitive mode is shown to be unique to circuit-based NPLLs. Concentrating on temporal decoding in the vibrissal system of rats, we conclude that both the nature of the information processing task and the response characteristics suggest that the computation is sensitive to the co-phase. Consequently, we suggest that the underlying thalamocortical loop should implement a circuit-based NPLL.
机译:有节奏的主动触摸(例如,拂动)会唤起周期性的参考尖峰信号,通过该信号可以解释新的刺激的时机(例如,当晶须撞到外部物体时)。先前的工作支持以下假设,即,由晶须引起的尖峰序列会在振动系统中带动锁相环(NPLL)的神经实现。在这里,我们扩展了这项工作,并探讨了所携带的NPLL如何解码新颖的,接触诱导的刺激的延迟并促进对象定位。我们考虑两种分别基于单个神经元或神经电路的NPLL实现,并评估所得的时间解码能力。根据NPLL的结构,它可以锁定在相敏或同相敏感模式下,该模式分别相对于当前周期或下一个周期的开始对输入的时序敏感。同相敏感模式显示为基于电路的NPLL独有。专注于大鼠振动系统中的时间解码,我们得出结论,信息处理任务的性质和响应特性都表明计算对同相敏感。因此,我们建议基础的丘脑皮层环路应实现基于电路的NPLL。

著录项

  • 来源
    《Neural computation》 |2006年第7期|p.1611-1636|共26页
  • 作者单位

    Sensory-Motor Integration Laboratory, Technion Institute of Technology, Haifa, Israel;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
  • 关键词

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