Departments of Psychology,
Departments of Psychology,Psychiatry, andNeurosciences, University of California, San Diego, La Jolla, CA 92093;Veterans Affairs Medical Center, San Diego, CA 92161;
Proc Natl Acad Sci U S A. 2014 Jul 1;111(26):9621-6. doi: 10.1073/pnas.1408365111. Epub 2014 Jun 16.
Neurocomputational models hold that sparse distributed coding is the most efficient way for hippocampal neurons to encode episodic memories rapidly. We investigated the representation of episodic memory in hippocampal neurons of nine epilepsy patients undergoing intracranial monitoring as they discriminated between recently studied words (targets) and new words (foils) on a recognition test. On average, single units and multiunits exhibited higher spike counts in response to targets relative to foils, and the size of this effect correlated with behavioral performance. Further analyses of the spike-count distributions revealed that (i) a small percentage of recorded neurons responded to any one target and (ii) a small percentage of targets elicited a strong response in any one neuron. These findings are consistent with the idea that in the human hippocampus episodic memory is supported by a sparse distributed neural code.
神经计算模型认为，稀疏分布式编码是海马体神经元快速编码情景记忆的最有效方式。我们在九名接受颅内监测的癫痫患者中进行了研究，当他们在识别测试中区分最近学习过的单词（目标）和新单词（干扰）时，研究了海马体神经元中情景记忆的表示。平均而言，单个单位和多单位在对目标的反应中表现出比干扰更高的尖峰计数，并且这种效应的大小与行为表现相关。对尖峰计数分布的进一步分析表明，（i）一小部分记录的神经元对任何一个目标都有反应，（ii）一小部分目标在任何一个神经元中引起强烈的反应。这些发现与这样的观点一致，即情景记忆在人类海马体中是由稀疏分布式神经编码支持的。
