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首页> 外文会议>Computational Neuroscience Meeting (CNS'01) Jul, 2001 Monterey, California >Spike train analysis for single trial data
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Spike train analysis for single trial data

机译：尖峰火车分析单次试验数据

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摘要

Traditional methods in neural data analysis are not appropriate for analyzing the spike train of a single experimental trial. We show that, by constructing a model of firing statistics, a more accurate estimate of the firing rate for a single spike train can be obtained. The model is based on the assumption that the neuron's spikes are generated by a non-homogeneous Poisson process which follows Markovian dynamics. We test the method by reconstructing the input stimulus based on the neurons' responses either on the raw spike data or the firing rate estimate. The spike data were recorded from macaque V1 neurons in response to a sinewave grating undergoing pseudo-random walk. For a large percentage of the cells studied, the reconstruction is significantly improved by using the estimated firing rate over the raw spikes, suggesting that estimated rate reflects more accurately the underlying state of the neurons.
机译：神经数据分析中的传统方法不适用于分析单个试验的峰值序列。我们表明，通过构建点火统计模型，可以获得单个峰值列车的点火速率的更准确估计。该模型基于以下假设：神经元的尖峰是由遵循马尔可夫动力学的非均匀泊松过程生成的。我们通过基于原始尖峰数据或点火速率估计值的神经元响应重建输入刺激来测试该方法。响应于正弦波光栅进行伪随机游走，从猕猴V1神经元记录了峰值数据。对于所研究的大部分细胞，通过使用原始刺突的估计发射速率可以显着改善重建，这表明估计速率可以更准确地反映神经元的基础状态。

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