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Spiking Neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage?

机译:尖刺神经元:是否足以比较膜电压波动响应的重合系数?

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Similarity between two spike trains is generally estimated using a 'coincidence factor'. This factor relies on counting coincidences of firing-times for spikes in a given time window. However, in cases where there are significant fluctuations in membrane voltages, this uni-dimensional view is not sufficient. Results in this paper show that a two-dimensional approach taking both firing-time and the magnitude of spikes is necessary to determine similarity between spike trains. It is observed that the difference between the lower-bound limit of faithful behaviour and the reference inter-spike interval (ISI) reduces with the increase in the ISI of the input spike train. This indicates that spike trains generated by two highly-varying currents have a high coincidence factor thus indicating higher similarity - a limitation imposed due to a one-dimensional comparison approach. These results are analysed based on the responses of a Hodgkin-Huxley neuron, where the synaptic input induces fluctuations in the output membrane voltage. The requirement for a two-dimensional analysis is further supported by a clustering algorithm which differentiates between two visually-distinct responses as opposed to coincidence-factor.
机译:通常使用“巧合因子”估计两个尖峰列车之间的相似性。这个因素依赖于在给定的时间窗口中计算尖峰的射击时间吻合。然而,在膜电压中存在显着波动的情况下,这种单维视图不够。本文的结果表明,需要采用射击时间和尖峰幅度的二维方法,以确定尖峰列车之间的相似性。观察到,忠实行为的下限限制与参考间隔间隔(ISI)之间的差异随着输入尖峰列车的ISI的增加而减少。这表明由两个高度电流产生的尖峰列车具有高巧合因子,从而表明较高的相似性 - 由于一维的比较方法引起的限制。基于Hodgkin-Huxley神经元的响应来分析这些结果,其中突触输入引起输出膜电压的波动。通过聚类算法进一步支持对二维分析的要求,该聚类算法在两个视觉上不同的响应之间不同于符合率因子。

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