NeuroExplorer Analysis Algorithm
内容目录

简单记录了neuroexplorer软件中自带的一些算法

Type name description remark
Spike Train Structure Rate Histograms 1、对于spike trains,计算其每个bin中event数量
2、对于连续信号,计算每个bin的平均值
Firing Rates 指定时间段,并计算该时间段内的发射速率(事件数/时间(s)),与Rate Histograms类似
Autocorrelograms 计算给定数据的Autocorrelograms(即每个event前后一定范围内的spike数量统计)
Autocorrelograms Versus Time 将数据按时间分段,单独计算每段的Autocorrelograms后绘图(x轴为时间,y轴为对应时间段的Autocorrelograms) 代码已复现
Interspike Interval Histogramss 计算在长度在[$t_0,t_0+t$]范围内的ISI的数量。并提到了用log bins去计算
Burst Analysis 分析spike trains 中的burst NeuroExplorer引用的论文中提到了用Posion surprise去寻找burst。

Joint ISI 计算Joint ISI Distribution
Poincare Maps 对于每个发生在t[i]的spike,在((t[i] - t[i-1], t[i-1] - t[i-2]))上绘制点
Hazard Analysis
CV2 Analysis 计算spike trains的coefficient of variation(变异系数) 用$CV_2$来计算,提高了特定情况下变异系数的准确性
代码已复现
Power Spectral Densities
Spectrogram Analysis
Spike Train Visualizations Rasters 绘制spike与时间的光栅图
Cumulative Activity Graphs 绘制spike数量的累积图像(x轴为时间,y轴为累积spike数量)
Instant Frequency 计算瞬时频率,即(1/(t[i] - t[i-1])),其中t[i]是第i个spike的时间点
Interspike Intervals vs. Time 对于,第i个spike,其时间为t[i],绘制(t[i],t[i] - t[i-1])点
Dependencies between Channels Crosscorrelograms 与Autocorrelograms类似,event时间由自身对应时间变为另一个spike trains的对应时间。并提供了多种标准化方法
Perievent Histograms 与Crosscorrelograms一样
PSTH Versus Time 用滑动窗口计算多个PSTH
Trial Bin Counts
Perievent Rasters 一个list相对于另一个list的对比格栅图
Perievent Firing Rates
Joint PSTH 计算Joint PSTH 代码已复现
Epoch Counts
Correlations With Continuous Variable
Coherence Analysis
Perievent Spectrograms
Regularity Analysis
Synchrony vs. Time
Analysis of Head Direction Cells
Firing Phase
Place Cell Analysis
Principal Component Analysis
Reverse Correlation
Analyses of Continuous Data Band Energy versus Time
Coherence Analysis for Continuous Variables
Find Oscillations
Find Ripples
Perievent Rasters for Continuous
Phase Analysis via Hibert Transform
Power Spectral Densities for Continuous Variables
Single Trial Spectrum Analysis
Analyses of Waveforms Sort Spikes
Waveform Comparison
Custom Analysis Python-based Analysis
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