Hampson Robert E, Robinson Brian S, Marmarelis Vasilis Z, Deadwyler Sam A, Berger Theodore W
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1620-1623. doi: 10.1109/EMBC.2016.7591023.
To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.
为了理解记忆信息如何在海马体中编码，我们构建了分类模型，以从癫痫患者执行依赖记忆的延迟匹配样本任务时记录的海马CA3和CA1尖峰时空模式中解码记忆特征。分类模型由一组用于从尖峰模式中提取记忆特征的B样条基函数和一个用于生成记忆特征二元分类输出的稀疏逻辑回归分类器组成。结果表明，尽管由于样本量小导致预测准确性存在高度变异性，但分类模型仍可以提取与记忆任务类型和任务中使用的样本图像类别相关的大量记忆信息。这些结果支持了记忆在海马体活动中编码的假设，并对海马体记忆假体的开发具有重要意义。
