Robust and brain-like working memory through short-term synaptic plasticity
Name
journal.pcbi.1010776.pdf
Description
Published version
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2.94 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Kozachkov, Leo
Tauber, John
Lundqvist, Mikael
Brincat, Scott L
Slotine, Jean-Jacques
Miller, Earl K
Date Issued
December 27, 2022
Journal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Kozachkov, Leo, Tauber, John, Lundqvist, Mikael, Brincat, Scott L, Slotine, Jean-Jacques et al. 2022. "Robust and brain-like working memory through short-term synaptic plasticity." PLOS Computational Biology, 18 (12).
Version
Final published version
Abstract
Working memory has long been thought to arise from sustained spiking/attractor dynamics. However, recent work has suggested that short-term synaptic plasticity (STSP) may help maintain attractor states over gaps in time with little or no spiking. To determine if STSP endows additional functional advantages, we trained artificial recurrent neural networks (RNNs) with and without STSP to perform an object working memory task. We found that RNNs with and without STSP were able to maintain memories despite distractors presented in the middle of the memory delay. However, RNNs with STSP showed activity that was similar to that seen in the cortex of a non-human primate (NHP) performing the same task. By contrast, RNNs without STSP showed activity that was less brain-like. Further, RNNs with STSP were more robust to network degradation than RNNs without STSP. These results show that STSP can not only help maintain working memories, it also makes neural networks more robust and brain-like.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1010776
