ArticleNature neuroscience2024
Maintaining and updating accurate internal representations of continuous variables with a handful of neurons.
Article in Nature neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026Article
- Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits.Neural computation · 2026Article
- Prediction of neural activity in connectome-constrained recurrent networks.Nature neuroscience · 2025Article
- Explicit error coding can mediate gain recalibration in continuous bump attractor networks.Nature communications · 2025Article
- Disinhibition of a recurrent attractor gates a persistent goal signal for navigation.bioRxiv : the preprint server for biology · 2025Article
- Topological Neuroscience: Linking Circuits to Function.Annual review of neuroscience · 2025Review
- Co-release of opposing signaling molecules controls the escalation and release of aggression.bioRxiv : the preprint server for biology · 2025Article
- A computational model for angular velocity integration in a locust heading circuit.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many animals rely on persistent internal representations of continuous variables for working memory, navigation, and motor control. Existing theories typically assume that large networks of neurons are required to maintain such representations accurately; networks with few neurons are thought to generate discrete representations. However, analysis of two-photon calcium imaging data from tethered flies walking in darkness suggests that their small head-direction system can maintain a surprisingly continuous and accurate representation. We thus ask whether it is possible for a small network to generate a continuous, rather than discrete, representation of such a variable. We show analytically that even very small networks can be tuned to maintain continuous internal representations, but this comes at the cost of sensitivity to noise and variations in tuning. This work expands the computational repertoire of small networks, and raises the possibility that larger networks could represent more and higher-dimensional variables than previously thought.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.