ArticleeNeuro
A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Stability through plasticity: Finding robust memories through representational drift.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Representational drift in barrel cortex is receptive field dependent.bioRxiv : the preprint server for biology · 2023Article
- A stable sensory map emerges from a dynamic equilibrium of neurons with unstable tuning properties.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2023Article
- Self-healing codes: How stable neural populations can track continually reconfiguring neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning.PloS one · 2022Article
- Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- Spine dynamics in the brain, mental disorders and artificial neural networks.Nature reviews. Neuroscience · 2021Review
- Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms.Sensors (Basel, Switzerland) · 2021Article
- Mechanism of Action of Dengzhan Shengmai in Regulating Stroke from an Inflammatory Perspective: A Preliminary Analysis of Network Pharmacology.Evidence-based complementary and alternative medicine : eCAM · 2021Article
- Adaptive Extreme Edge Computing for Wearable Devices.Frontiers in neuroscience · 2021Review
- Review
- Article
- Adaptive Tuning Curve Widths Improve Sample Efficient Learning.Frontiers in computational neuroscience · 2020Article
- Noise Helps Optimization Escape From Saddle Points in the Synaptic Plasticity.Frontiers in neuroscience · 2020Article
- Editorial: The Embodied Brain: Computational Mechanisms of Integrated Sensorimotor Interactions With a Dynamic Environment.Frontiers in computational neuroscience · 2020Article
- Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring.Frontiers in computational neuroscience · 2020Article
- Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures.Frontiers in neuroscience · 2020Article
- Causes and consequences of representational drift.Current opinion in neurobiology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.
Indexed as
Identifiers
What OpenQuestion holds
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.