Cited by 163Neural dynamics and brain functionMemory and Neural MechanismsAdvanced Memory and Neural Computing

From Distributed Resources to Limited Slots in Multiple-Item Working Memory: A Spiking Network Model with Normalization

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Wei, Ziqiang, Wang, Xiao-Jing, Wang, Da-Hui·2012·The Journal of Neuroscience

Abstract

Recent behavioral studies have given rise to two contrasting models for limited working memory capacity: a “discrete-slot” model in which memory items are stored in a limited number of slots, and a “shared-resource” model in which the neural representation of items is distributed across a limited pool of resources. To elucidate the underlying neural processes, we investigated a continuous network model for working memory of an analog feature. Our model network fundamentally operates with a shared resource mechanism, and stimuli in cue arrays are encoded by a distributed neural population. On the other hand, the network dynamics and performance are also consistent with the discrete-slot model, because multiple objects are maintained by distinct localized population persistent activity patterns (bump attractors). We identified two phenomena of recurrent circuit dynamics that give rise to limited working memory capacity. As the working memory load increases, a localized persistent activity bump may either fade out (so the memory of the corresponding item is lost) or merge with another nearby bump (hence the resolution of mnemonic representation for the merged items becomes blurred). We identified specific dependences of these two phenomena on the strength and tuning of recurrent synaptic excitation, as well as network normalization: the overall population activity is invariant to set size and delay duration; therefore, a constant neural resource is shared by and dynamically allocated to the memorized items. We demonstrate that the model reproduces salient observations predicted by both discrete-slot and shared-resource models, and propose testable predictions of the merging phenomenon.

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Authors
Wei, Z., Wang, X.-J., & Wang, D.-H.
Affiliations
  • Howard Hughes Medical Institute (US)
  • Yale University (US)
  • Beijing Normal University (CN)
Venue
The Journal of Neuroscience
Published
2012-08-15
Volume
32
Issue
33
Pages
11228-11240
ISSN
  • 0270-6474 (ISSN-L)
  • 1529-2401
OpenAlex
W1980372308
Cited by
163
References
65
Citations by year
2026: 5 · 2025: 9 · 2024: 12 · 2023: 15 · 2022: 8 · 2021: 13 · 2020: 8 · 2019: 9 · 2018: 14 · 2017: 22 · 2016: 9 · 2015: 11 · 2014: 11 · 2013: 15 · 2012: 2
FWCI
6.38
Type
journal article
Language
en
Open access
Locations
  • Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Howard Hughes Medical Institute
  • Kavli Foundation
  • National Natural Science Foundation of China (60974075, 91132702)
  • Beijing Normal University
  • National Institutes of Health
  • Fundamental Research Funds for the Central Universities
  • National Institute of Mental Health (MH 062349, R01MH062349)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Keywords
working memory, spiking neural networks, normalization, persistent activity, recurrent excitation, memory load, population coding
MeSH terms
Action Potentials, Attention, Computer Simulation, Cues, Humans, Memory, Short-Term, Models, Neurological, Neural Pathways, Neurons, Photic Stimulation, Psychophysics, Time Factors, Signal Detection, Psychological, Nonlinear Dynamics

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