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A NEURONAL LEARNING RULE FOR SUB-MILLISECOND TEMPORAL CODING

机译：亚微秒级时间编码的神经学习规则

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A PARADOX that exists in auditory and electrosensory neural systems(1,2) is that they encode behaviourally relevant signals in the range of a few microseconds with neurons that are at least one order of magnitude slower, The importance of temporal coding in neural information processing is not clear yet(3-8), A central question is whether neuronal firing can be more precise than the time constants of the neuronal processes involved(9), Here we address this problem using the auditory system of the barn owl as an example, We present a modelling study based on computer simulations of a neuron in the laminar nucleus. Three observations explain the paradox. First, spiking of an 'integrate-and-fire' neuron driven by excitatory postsynaptic potentials with a width at half-maximum height of 250 mu s, has an accuracy of 25 mu s if the presynaptic signals arrive coherently. Second, the necessary degree of coherence in the signal arrival times can be attained during ontogenetic development by virtue of an unsupervised hebbian learning rule, Learning selects connections with matching delays from a broad distribution of axons with random delays, Third, the learning rule also selects the correct delays from two independent groups of inputs, for example, from the left and right ear. [References: 30]
机译：听觉和电感觉神经系统（1,2）中存在的PARADOX是它们在几微秒的范围内用至少慢一个数量级的神经元编码与行为相关的信号，时间编码在神经信息处理中的重要性尚不清楚（3-8），一个中心问题是神经元放电是否比涉及的神经元过程的时间常数更精确（9），这里我们以谷仓猫头鹰的听觉系统为例解决这个问题。 ，我们基于层状核中神经元的计算机模拟提出了一种建模研究。三个观察结果解释了这一悖论。首先，由兴奋性突触后电位驱动的“整合并发射”神经元的峰值最大半高为250μs，如果突触前信号连贯到达，其准确度为25μs。其次，通过无监督的hebbian学习规则，可以在本体发育过程中获得信号到达时间的必要一致性。Learning从具有随机延迟的广泛轴突分布中选择具有匹配延迟的连接，其次，学习规则也选择来自两个独立输入组的正确延迟，例如来自左耳和右耳的延迟。 [参考：30]

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