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

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 discr...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2014-07, Vol.111 (26), p.9621-9626
Main Authors: Wixted, John T., Squire, Larry R., Jang, Yoonhee, Papesh, Megan H., Goldinger, Stephen D., Kuhn, Joel R., Smith, Kris A., Treiman, David M., Steinmetz, Peter N.
Format: Article
Language:English
Subjects:
Amygdala
Biological Sciences
Epilepsy
Epilepsy - physiopathology
Frequency distribution
Gaussian distributions
Hippocampus
Hippocampus - physiology
Humans
Memory
Memory, Episodic
Models, Neurological
Neurons
Neurophysiological Monitoring
Neuropsychological Tests
Recognition memory
Signal detection
T score
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1408365111