Combining ESOMs Trained on a Hierarchy of Feature Subsets for Single-Trial Decoding of LFP Responses in Monkey Area V4
Combining ESOMs Trained on a Hierarchy of Feature Subsets for Single-Trial Decoding of LFP Responses in Monkey Area V4
复制标题
组合在特征子集层次结构上训练的 ESOM，用于 Monkey Area V4 中 LFP 响应的单次试验解码
DOI：
10.1007/978-3-642-13232-2_67
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. V. Hulle
中科院分区：
文献类型：
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作者：
N. Manyakov;Jonas Poelmans;R. Vogels;M. V. Hulle
我们开发和联合收割机地形图训练的不同组合的功能子集的可视化和分类的事件相关的反应记录与多电极阵列长期植入在视觉皮层区V4的恒河猴。在连续的训练期间，猴子接受了经典条件反射范式的训练，其中一种刺激始终与流体奖励配对，而另一种刺激则没有。我们选择了三个类别的功能：时频分析，电极之间的相位同步，并在阵列中传播波。采用涌现自组织映射（ESOM）来探讨单次译码的可行性。由于特征空间的有效维数相当高，因此在从三个特征类别的不同组合中选择的特征上训练一系列ESOM。对于每个训练的ESOM，分类器被开发，并且不同ESOM的分类器被组合以最大化单次尝试解码性能。
We develop and combine topographic maps trained on different combinations of feature subsets for visualizing and classifying event-related responses recorded with a multi-electrode array chronically implanted in the visual cortical area V4 of a rhesus monkey. The monkey was trained, during consecutive training sessions, in a classical conditioning paradigm in which one stimulus was consistently paired with a fluid reward and another stimulus not. We opted for features from three categories: time-frequency analysis, phase synchronization between electrodes, and propagating waves in the array. The Emergent Self Organizing Map (ESOM) was used to explore the feasibility of single-trial decoding. Since the effective dimensionality of the feature space is rather high, a series of ESOMs was trained on features selected from different combinations of the three feature categories. For each trained ESOM, a classifier was developed, and classifiers of different ESOMs were combined so as to maximize the single-trial decoding performance.