Sequential Optimal Design of Neurophysiology Experiments
Sequential Optimal Design of Neurophysiology Experiments
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DOI：
10.1162/neco.2008.08-07-594
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发表时间：
2009-03-01
影响因子：
2.9
通讯作者：
Paninski, Liam
中科院分区：
文献类型：
--
作者：
Lewi, Jeremy;Butera, Robert;Paninski, Liam
自适应优化实验有可能显着减少建立神经系统参数统计模型所需的试验次数。然而，神经生理学的自适应方法的应用受到严重的计算挑战的限制。由于大多数神经元是高维系统，优化神经生理学实验需要在真实的时间内计算高维积分和优化。在这里，我们提出了一种快速算法，通过最大化数据和广义线性模型（GLM）的未知参数之间的互信息来选择信息量最大的刺激，我们要拟合神经元的活动。我们依赖于重要的对数正态性和渐近正态性的后验属性，以方便所需的计算。我们的算法只需要低秩矩阵操作和二维搜索来选择最佳的刺激。这些操作的平均运行时间与GLM的维数成二次方，使得实时自适应实验设计即使对于高维刺激和参数空间也是可行的。例如，我们需要大约10毫秒在台式计算机上优化100维刺激。尽管使用了一些近似，使算法的效率，我们的算法渐近降低的不确定性的模型参数的速率等于由渐近分析预测的最大速率。仿真结果表明，通过最大化的互信息挑选刺激可以加快收敛到最优值的参数的数量级相比，使用随机（非自适应）的刺激。最后，将我们的设计过程应用到真实的神经生理学实验中，需要解决我们期望在神经反应中看到的非平稳性;我们的算法可以有效地处理由于尖峰历史效应和神经元活动中的缓慢非系统漂移引起的快速适应。
Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high-dimensional systems, optimizing neurophysiology experiments requires computing high-dimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) that we want to fit to the neuron's activity. We rely on important log concavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only low-rank matrix manipulations and a two-dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making real-time adaptive experimental design feasible even for high-dimensional stimulus and parameter spaces. For example, we require roughly 10 milliseconds on a desktop computer to optimize a 100-dimensional stimulus. Despite using some approximations to make the algorithm efficient, our algorithm asymptotically decreases the uncertainty about the model parameters at a rate equal to the maximum rate predicted by an asymptotic analysis. Simulation results show that picking stimuli by maximizing the mutual information can speed up convergence to the optimal values of the parameters by an order of magnitude compared to using random (nonadaptive) stimuli. Finally, applying our design procedure to real neurophysiology experiments requires addressing the nonstationarities that we would expect to see in neural responses; our algorithm can efficiently handle both fast adaptation due to spike history effects and slow, nonsystematic drifts in a neuron's activity.