Hampson Robert E, Deadwyler Sam A, Berger Theodore W
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1046-1049. doi: 10.1109/EMBC.2017.8037006.
To understand how memories are encoded in the hippocampus, we build memory decoding models to classify visual memories based on hippocampal activities in human. Model inputs are spatio-temporal patterns of spikes recorded in the hippocampal CA3 and CA1 regions of epilepsy patients performing a delayed match-to-sample (DMS) task. Model outputs are binary labels indicating categories and features of sample images. To solve the super high-dimensional estimation problem with short data length, we develop a multi-trial, sparse model estimation method utilizing B-spline basis functions with a large range of temporal resolutions and a regularized logistic classifier. Results show that this model can effectively avoid overfitting and provide significant amount of prediction to memory categories and features using very limited number of data points. Stable estimation of sparse classification function matrices for each label can be obtained with this multi-resolution, multi-trial procedure. These classification models can be used not only to predict memory contents, but also to design optimal spatio-temporal patterns for eliciting specific memories in the hippocampus, and thus have important implications to the development of hippocampal memory prostheses.
为了理解记忆如何在海马体中编码，我们构建了记忆解码模型，以根据人类海马体活动对视觉记忆进行分类。模型输入是癫痫患者在执行延迟匹配样本（DMS）任务时海马体CA3和CA1区域记录的尖峰的时空模式。模型输出是表示样本图像类别和特征的二元标签。为了解决数据长度短的超高维估计问题，我们开发了一种多试验、稀疏模型估计方法，该方法利用具有大范围时间分辨率的B样条基函数和正则化逻辑分类器。结果表明，该模型可以有效避免过拟合，并使用非常有限的数据点对记忆类别和特征提供大量预测。通过这种多分辨率、多试验程序，可以获得每个标签的稀疏分类函数矩阵的稳定估计。这些分类模型不仅可以用于预测记忆内容，还可以用于设计最佳时空模式以在海马体中引发特定记忆，因此对海马体记忆假体的开发具有重要意义。
