ArticleJournal of computational neuroscience2014
Encoding certainty in bump attractors.
Article in Journal of computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A bio-inspired minimal model for non-stationary K-armed bandits.Biological cybernetics · 2026Article
- Robust maintenance of both stimulus location and amplitude in a working memory model based on dendritic bistability.bioRxiv : the preprint server for biology · 2026Article
- Probabilistic working memory representations in human cortex guide behavior.bioRxiv : the preprint server for biology · 2025Article
- Multimodal cue integration and learning in a neural representation of head direction.Nature neuroscience · 2025Article
- Robustly encoding certainty in a metastable neural circuit model.Physical review. E · 2024Article
- Representation and computation in visual working memory.Nature human behaviour · 2024Review
- An oscillatory mechanism for multi-level storage in short-term memory.Communications biology · 2023Article
- Bayesian inference in ring attractor networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A dynamic neural field model of continuous input integration.Biological cybernetics · 2021Article
- Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons.Journal of computational neuroscience · 2020Article
- Synaptic efficacy shapes resource limitations in working memory.Journal of computational neuroscience · 2018Article
- Synaptic mechanisms of interference in working memory.Scientific reports · 2018Article
- Analysis of an Attractor Neural Network's Response to Conflicting External Inputs.Journal of mathematical neuroscience · 2018Article
- Balanced cortical microcircuitry for spatial working memory based on corrective feedback control.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2014Article
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Authors and funding
3 authors.
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Abstract
Persistent activity in neuronal populations has been shown to represent the spatial position of remembered stimuli. Networks that support bump attractors are often used to model such persistent activity. Such models usually exhibit translational symmetry. Thus activity bumps are neutrally stable, and perturbations in position do not decay away. We extend previous work on bump attractors by constructing model networks capable of encoding the certainty or salience of a stimulus stored in memory. Such networks support bumps that are not only neutrally stable to perturbations in position, but also perturbations in amplitude. Possible bump solutions then lie on a two-dimensional attractor, determined by a continuum of positions and amplitudes. Such an attractor requires precisely balancing the strength of recurrent synaptic connections. The amplitude of activity bumps represents certainty, and is determined by the initial input to the system. Moreover, bumps with larger amplitudes are more robust to noise, and over time provide a more faithful representation of the stored stimulus. In networks with separate excitatory and inhibitory populations, generating bumps with a continuum of possible amplitudes, requires tuning the strength of inhibition to precisely cancel background excitation.
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