ArticleeLife2017
Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Article
- Backpropagation through space, time and the brain.Nature communications · 2025Article
- Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks.Nature communications · 2025Article
- A neural implementation model of feedback-based motor learning.Nature communications · 2025Article
- Specific connectivity optimizes learning in thalamocortical loops.Cell reports · 2024Article
- Fast learning without synaptic plasticity in spiking neural networks.Scientific reports · 2024Article
- Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.Nature neuroscience · 2024Article
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- Article
- Learning cortical hierarchies with temporal Hebbian updates.Frontiers in computational neuroscience · 2023Article
- Article
- Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals.Scientific reports · 2022Article
- Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors.Scientific reports · 2021Article
- Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor.International journal of environmental research and public health · 2021Article
- Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces.Frontiers in neuroscience · 2021Article
- Learning probabilistic neural representations with randomly connected circuits.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks.PLoS computational biology · 2020Article
- Inhibitory Plasticity: From Molecules to Computation and Beyond.International journal of molecular sciences · 2020Review
- Learning to represent signals spike by spike.PLoS computational biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.
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.