Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
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DOI：
10.1109/embc.2015.7319261
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
D. Song;Brian S. Robinson;R. Hampson;V. Marmarelis;S. Deadwyler;T. Berger
中科院分区：
文献类型：
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作者：
D. Song;Brian S. Robinson;R. Hampson;V. Marmarelis;S. Deadwyler;T. Berger
为了构建恢复记忆功能的海马假体，我们建立了人类海马的多输入多输出（MIMO）非线性动力学模型。从癫痫患者的海马CA 3和CA 1区域记录棘波序列，执行记忆依赖性延迟匹配到样本任务。分别使用CA 3和CA 1尖峰序列作为输入和输出，二阶稀疏广义Laguerre-Volterra模型估计与组套索和局部坐标下降方法捕捉潜在的尖峰序列变换的非线性动力学。这些模型可以准确地预测的基础上正在进行的CA 3的棘波序列的CA 1，从而将作为海马记忆假体的计算基础。
In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed match-to-sample task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.