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首页> 外文会议>Annual Computational Neuroscience Meeting(CNS'02); 20020721-20020725; Chicago,IL; US >Burst firing improves the detection of weak signals in spike trains
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Burst firing improves the detection of weak signals in spike trains

机译：突发发射可改善对尖峰火车中微弱信号的检测

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摘要

The detection of weak sensory signals is an important aspect of neuronal information processing. Behaviorally relevant signals are often encoded as perturbations of on-going spiking activity in primary afferents. Here, we show that a biologically plausible model, the leaky integrate-and-fire (LIAF) neuron, is capable of efficient and reliable detection of a single spike added to baseline activity. Detection performance is dependent on the statistical properties of the spike train. For the type of statistics considered here, an LIAF neuron can distinguish between a correct detection by means of burst firing, whereas false alarms tend to result in isolated spikes. The methods are illustrated by an application to electrosensory afferents of weakly electric fish.
机译：弱感官信号的检测是神经元信息处理的重要方面。与行为相关的信号通常被编码为初级传入持续刺激活动的扰动。在这里，我们显示出生物学上可行的模型，即泄漏集成并发射（LIAF）神经元，能够高效，可靠地检测添加到基线活动中的单个峰值。检测性能取决于尖峰序列的统计属性。对于此处考虑的统计类型，LIAF神经元可以通过突发触发来区分正确的检测，而错误警报往往会导致孤立的尖峰。通过将其应用于弱电鱼的电感应传入来说明这些方法。

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