Temporal coding: Competition for coherence and new perspectives on assembly formation
Temporal coding: Competition for coherence and new perspectives on assembly formation
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时间编码：一致性竞争和程序集形成的新视角
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发表时间：
2009
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通讯作者：
T. Burwick
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作者：
T. Burwick
回顾了基于耦合相位模型振荡器网络（具有概括经典神经网络的幅度动力学）的时间编码建模的最新进展，并概述了未来研究的可能方向。审查的重点是通过所谓的加速补充同步的模型。后一种机制意味着在来自连接单元的更强和/或更一致的输入的情况下神经单元的相速度增加。假设赫布存储模式，结果表明，包含加速度会引入连贯性竞争，这对模式识别具有深远而有利的影响。时间组合（被理解为相干模式的锁相集）赢得了竞争，而其他模式则变得不相干或偏离状态。展望了包括抑制、分层处理和零延迟同步（尽管有时间延迟）的路径。我们还提到了与最近的神经生理学实验的可能关系，这些实验研究将兴奋性驱动重新编码为相移。
Recent progress towards modeling temporal coding based on networks of coupled phase model oscillators (with amplitude dynamics that generalizes classical neural networks) is reviewed and possible directions of future research are sketched. The review concentrates on models that complement synchronization with so-called acceleration. The latter mechanism implies an increased phase velocity of neural units in case of stronger and/or more coherent input from the connected units. Assuming Hebbian storage of patterns, it is demonstrated that the inclusion of acceleration introduces a competition for coherence that has a profound and favorable effect on pattern recognition. Temporal assemblies, understood as phase-locked sets of coherent patterns, win the competition, while the other patterns become de-coherent or off-state. Outlook is given on paths towards including inhibition, hierarchical processing, and zero-lag synchronization despite time delays. We also mention possible relations to recent neurophysiological experiments that study the recoding of excitatory drive into phase shifts.