Sparse and distributed coding of episodic memory in neurons of the human hippocampus

John T. Wixted, Larry R. Squire, Yoonhee Jang, Megan H. Papesh, Stephen D. Goldinger, Joel R. Kuhn, Kris A. Smith, David M. Treiman, Peter N. Steinmetz

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


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.

Original languageEnglish
Pages (from-to)9621-9626
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number26
StatePublished - Jul 1 2014


  • Amygdala
  • Intracranial recording
  • Recognition memory


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