ArticlePLoS computational biology2021
Slow manifolds within network dynamics encode working memory efficiently and robustly.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- A proposal for realizing cognitive functions using traveling waves, phase-locked and synchronized patterns, holography, neural mixing, and single sideband communications.Frontiers in computational neuroscience · 2026Article
- Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning.Cognitive neurodynamics · 2024Article
- Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention.Neural computation · 2024Article
- Hamiltonian energy in a modified Hindmarsh-Rose model.Frontiers in network physiology · 2024Article
- Multiplexing working memory and time in the trajectories of neural networks.Nature human behaviour · 2023Article
- Synthesizing network dynamics for short-term memory of impulsive inputs.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.
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Registered trials
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