Extraction and restoration of hippocampal spatial memories with non-linear dynamical modeling.
Extraction and restoration of hippocampal spatial memories with non-linear dynamical modeling.
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用非线性动力学建模提取和恢复海马空间记忆。
DOI：
10.3389/fnsys.2014.00097
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发表时间：
2014
影响因子：
3
通讯作者：
Berger TW
中科院分区：
文献类型：
--
作者：
Song D;Harway M;Marmarelis VZ;Hampson RE;Deadwyler SA;Berger TW
为了构建能够替代海马体记忆功能的认知假体，必须对受损海马体区域的输入-输出功能进行建模，因此假体装置可以刺激下游海马体区域，例如，CA 1，与输出信号，例如，从正在进行的输入信号预测的CA 1尖峰序列，例如，CA 3尖峰序列，以及所识别的输入-输出函数，例如，CA 3-CA 1模型。此外，为了使下游区域基于恢复的输出信号形成适当的长期记忆，输出信号应该包含关于动物已经形成的记忆的足够信息。在这项研究中，我们验证了这一前提，应用回归和分类建模的时空模式的尖峰列车海马CA 3和CA 1的数据记录从大鼠执行记忆依赖性延迟非匹配到样本（DNMS）的任务。回归模型本质上是多输入多输出（MIMO）的非线性动力学模型的穗序列转换。它根据输入锋电位序列预测输出锋电位序列，从而恢复输出信号。此外，分类模型通过将时空模式与记忆事件相关联来解释信号。我们发现：（1）海马CA 3和CA 1尖峰序列都包含用于预测样本响应的位置的足够信息（即，更重要的是（2）通过MIMO模型从CA 3尖峰序列预测的CA 1尖峰序列也足以在单次试验的基础上预测位置。这些结果定量地表明，从海马的单一记录的中等数量，MIMO非线性动力学模型是能够提取和恢复的空间记忆信息的长期记忆的形成，从而可以作为海马记忆假体的计算基础。
To build a cognitive prosthesis that can replace the memory function of the hippocampus, it is essential to model the input-output function of the damaged hippocampal region, so the prosthetic device can stimulate the downstream hippocampal region, e.g., CA1, with the output signal, e.g., CA1 spike trains, predicted from the ongoing input signal, e.g., CA3 spike trains, and the identified input-output function, e.g., CA3-CA1 model. In order for the downstream region to form appropriate long-term memories based on the restored output signal, furthermore, the output signal should contain sufficient information about the memories that the animal has formed. In this study, we verify this premise by applying regression and classification modelings of the spatio-temporal patterns of spike trains to the hippocampal CA3 and CA1 data recorded from rats performing a memory-dependent delayed non-match-to-sample (DNMS) task. The regression model is essentially the multiple-input, multiple-output (MIMO) non-linear dynamical model of spike train transformation. It predicts the output spike trains based on the input spike trains and thus restores the output signal. In addition, the classification model interprets the signal by relating the spatio-temporal patterns to the memory events. We have found that: (1) both hippocampal CA3 and CA1 spike trains contain sufficient information for predicting the locations of the sample responses (i.e., left and right memories) during the DNMS task; and more importantly (2) the CA1 spike trains predicted from the CA3 spike trains by the MIMO model also are sufficient for predicting the locations on a single-trial basis. These results show quantitatively that, with a moderate number of unitary recordings from the hippocampus, the MIMO non-linear dynamical model is able to extract and restore spatial memory information for the formation of long-term memories and thus can serve as the computational basis of the hippocampal memory prosthesis.