ArticleScientific reports2017
Working Memory Requires a Combination of Transient and Attractor-Dominated Dynamics to Process Unreliably Timed Inputs.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation.Communications biology · 2026Article
- Recurrent neural networks with transient trajectory explain working memory encoding mechanisms.Communications biology · 2025Article
- Neurophysiological effective network connectivity supports a threshold-dependent management of dynamic working memory gating.iScience · 2024Article
- Slow manifolds within network dynamics encode working memory efficiently and robustly.PLoS computational biology · 2021Article
- Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks.Communications biology · 2021Article
- Fine Spike Timing in Hippocampal-Prefrontal Ensembles Predicts Poor Encoding and Underlies Behavioral Performance in Healthy and Malformed Brains.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi.Frontiers in neurorobotics · 2020Article
- The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations.Frontiers in neural circuits · 2020Article
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2 authors.
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Abstract
Working memory stores and processes information received as a stream of continuously incoming stimuli. This requires accurate sequencing and it remains puzzling how this can be reliably achieved by the neuronal system as our perceptual inputs show a high degree of temporal variability. One hypothesis is that accurate timing is achieved by purely transient neuronal dynamics; by contrast a second hypothesis states that the underlying network dynamics are dominated by attractor states. In this study, we resolve this contradiction by theoretically investigating the performance of the system using stimuli with differently accurate timing. Interestingly, only the combination of attractor and transient dynamics enables the network to perform with a low error rate. Further analysis reveals that the transient dynamics of the system are used to process information, while the attractor states store it. The interaction between both types of dynamics yields experimentally testable predictions and we show that this way the system can reliably interact with a timing-unreliable Hebbian-network representing long-term memory. Thus, this study provides a potential solution to the long-standing problem of the basic neuronal dynamics underlying working memory.
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