Cortical patch basis model for spatially extended neural activity
Cortical patch basis model for spatially extended neural activity
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DOI：
10.1109/tbme.2006.873743
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发表时间：
2006-09-01
影响因子：
4.6
通讯作者：
Wakai, Ronald T.
中科院分区：
文献类型：
--
作者：
Limpiti, Tulaya;Van Veen, Barry D.;Wakai, Ronald T.
提出了一种新的表示空间分布神经活动的源模型。感兴趣的信号被建模为源自一片皮层，并使用一组基函数表示。每个皮层块都有自己的一组基础，这使得可以表示块内的任意源活动。这与先前提出的假设补丁内的活动的特定分布的皮层补丁模型。我们提出了一个程序设计基地，最大限度地减少归一化均方表示误差，平均在不同的活动，分布在补丁。扩展现有的算法的基函数框架是简单的，并说明使用线性约束最小方差（LCMV）的空间滤波和最大似然信号估计/广义似然比检验（ML/GLRT）。为每个补丁选择的基础数量决定了表示精度和区分不同补丁的能力之间的权衡。我们建议选择最小数量的基地，满足约束的归一化均方表示精度。LCMV和ML/GLRT的错配分析表明，这是一个合适的策略选择的碱基数。使用，真实的和模拟诱发反应数据的补丁基础模型的有效性证明。我们表明，性能发生显着变化的基函数的数量变化，并通过允许适度的表示误差得到了非常好的结果。
A new source model for representing spatially distributed neural activity is presented. The signal of interest is modeled as originating from a patch of cortex and is represented using a set of basis functions. Each cortical patch has its own set of bases, which allows representation of arbitrary source activity within the patch. This is in contrast to previously proposed cortical patch models which assume a specific distribution of activity within the patch. We present a procedure for designing bases that minimize the normalized mean squared representation error, averaged over different activity, distributions within the patch. Extension of existing algorithms to the basis function framework is straightforward and is illustrated using linearly constrained minimum variance (LCMV) spatial filtering and maximum-likelihood signal estimation/generalized likelihood ratio test (ML/GLRT). The number of bases chosen for each patch determines a tradeoff between representation accuracy and the ability to differentiate between distinct patches. We propose choosing the minimum number of bases that satisfy a constraint on the normalized mean squared representation accuracy. A mismatch analysis for LCMV and ML/GLRT is presented to show that this is an appropriate strategy for choosing the number of bases. The effectiveness of the patch basis model is demonstrated using, real and simulated evoked response data. We show that significant changes in performance occur as the number of basis functions varies, and that very good results are obtained by allowing modest representation error.