Exact Bayesian bin classification: a fast alternative to Bayesian classification and its application to neural response analysis
Exact Bayesian bin classification: a fast alternative to Bayesian classification and its application to neural response analysis
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精确贝叶斯分类：贝叶斯分类的快速替代方案及其在神经响应分析中的应用
DOI：
10.1007/s10827-007-0039-5
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发表时间：
2008
影响因子：
1.2
通讯作者：
Peter Földiák
中科院分区：
文献类型：
--
作者：
Dominik M. Endres;Peter Földiák
我们调查的信号分类的一般问题，特别是，分配刺激标签的神经尖峰列车记录从单个皮层神经元。在涉及快速呈现刺激的实验中，找到有效的神经反应分类方法尤为重要。我们介绍了一种快速，准确的替代贝叶斯分类。而不是估计类条件密度p（x| y）（其中x是特征[s]的标量函数，y是类标签）并将它们转换为P（y| x）通过贝叶斯定理，直接评估该概率，无需近似。这是通过对x的所有可能的仓进行积分来实现的，仓的数量有上限。计算时间在观察到的数据点的数量和箱的数量上都是二次的。该算法还允许计算反馈信号，其可以用作推理的后续阶段的输入，例如神经网络训练。单个神经元的反应，从高层次的视觉皮层（区STSa）快速序列的复杂的视觉刺激进行了分析。信息潜伏期和反应时程与呈现时程呈非线性关系，表明神经处理速度与呈现速度相适应。
We investigate the general problem of signal classification and, in particular, that of assigning stimulus labels to neural spike trains recorded from single cortical neurons. Finding efficient ways of classifying neural responses is especially important in experiments involving rapid presentation of stimuli. We introduce a fast, exact alternative to Bayesian classification. Instead of estimating the class-conditional densitiesp(x|y) (wherexis a scalar function of the feature[s],ythe class label) and converting them toP(y|x) via Bayes’ theorem, this probability is evaluated directly and without the need for approximations. This is achieved by integrating over all possible binnings ofxwith an upper limit on the number of bins. Computational time is quadratic in both the number of observed data points and the number of bins. The algorithm also allows for the computation of feedback signals, which can be used as input to subsequent stages of inference, e.g. neural network training. Responses of single neurons from high-level visual cortex (area STSa) to rapid sequences of complex visual stimuli are analysed. Information latency and response duration increase nonlinearly with presentation duration, suggesting that neural processing speeds adapt to presentation speeds.