Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA.
Department of Physiology and Pharmacology, School of Medicine, Wake Forest University Winston-Salem, NC, USA.
Front Syst Neurosci. 2014 May 28;8:97. doi: 10.3389/fnsys.2014.00097. eCollection 2014.
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.
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,以便假体设备可以用从持续输入信号(例如 CA3 尖峰序列)中预测的输出信号(例如 CA1 尖峰序列)刺激下游的海马体区域,例如 CA1,并且识别输入-输出功能,例如 CA3-CA1 模型。为了使下游区域能够根据恢复的输出信号形成适当的长期记忆,此外,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,我们通过将尖峰序列的时空模式的回归和分类建模应用于大鼠执行记忆依赖性延迟非匹配样本(DNMS)任务时记录的海马体 CA3 和 CA1 数据,验证了这一前提。回归模型本质上是尖峰序列转换的多输入多输出(MIMO)非线性动力模型。它根据输入尖峰序列预测输出尖峰序列,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)海马体 CA3 和 CA1 尖峰序列都包含足够的信息来预测 DNMS 任务期间样本反应的位置(即左右记忆);更重要的是(2)通过 MIMO 模型从 CA3 尖峰序列预测的 CA1 尖峰序列也足以在单次试验的基础上进行预测。这些结果定量地表明,用来自海马体的中等数量的单元记录,MIMO 非线性动力模型能够提取和恢复用于形成长期记忆的空间记忆信息,因此可以作为海马体记忆假体的计算基础。