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From Distributed Resources to Limited Slots in Multiple-Item Working Memory: A Spiking Network Model with Normalization
Open accessAbstract
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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“Computational models suggest that the maintenance of multiple items within a WM circuit depends on the strength of lateral inhibition (Edin et al. 2009) and on the ability to keep activity patterns separated from each other (Wei et al. 2012).”
From Linking microcircuit dysfunction to cognitive impairment: Effects of disinhibition associated with schizophrenia in a cortical working memory model (2014) · cites this paper as Wei et al. 2012
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Metadata
- 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
- Publisher
- Society for Neuroscience
- Published
- 2012-08-15
- Volume
- 32
- Issue
- 33
- Pages
- 11228-11240
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3433498
- 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
- hybrid
- doi.org
- Licence
- 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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