论文标题
从坏MLE中拯救神经尖峰火车模型
Rescuing neural spike train models from bad MLE
论文作者
论文摘要
拟合自回归尖峰火车模型的标准方法是最大程度地提高一步预测的可能性。这种最大似然估计（MLE）通常会导致模型在递归生成一个以上的步骤时性能较差。此外，生成的尖峰火车可能无法捕获数据的重要功能，甚至显示出发射率不同。为了减轻这一点，我们建议直接最大程度地减少使用Spike Train内核的神经记录和模型产生的尖峰火车之间的差异。我们开发了一种随机优化内核引起的最大平均差异的方法。对真实和合成神经数据进行的实验验证了所提出的方法，表明它导致了行为良好的模型。使用尖峰列车内核的不同组合，我们表明我们可以控制不同特征之间的权衡，这对于处理模型不匹配至关重要。
The standard approach to fitting an autoregressive spike train model is to maximize the likelihood for one-step prediction. This maximum likelihood estimation (MLE) often leads to models that perform poorly when generating samples recursively for more than one time step. Moreover, the generated spike trains can fail to capture important features of the data and even show diverging firing rates. To alleviate this, we propose to directly minimize the divergence between neural recorded and model generated spike trains using spike train kernels. We develop a method that stochastically optimizes the maximum mean discrepancy induced by the kernel. Experiments performed on both real and synthetic neural data validate the proposed approach, showing that it leads to well-behaving models. Using different combinations of spike train kernels, we show that we can control the trade-off between different features which is critical for dealing with model-mismatch.
