ArticlePLoS computational biology2020
Learning spatiotemporal signals using a recurrent spiking network that discretizes time.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network.iScience · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Hippocampal sequences represent working memory and implicit timing.Cell reports · 2025Article
- Competition and cooperation of assembly sequences in recurrent neural networks.PLoS computational biology · 2025Article
- Emergence of brain-inspired small-world spiking neural network through neuroevolution.iScience · 2024Article
- A neural basis for learning sequential memory in brain loop structures.Frontiers in computational neuroscience · 2024Article
- Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization.Frontiers in neuroscience · 2024Article
- Neural Sequences and the Encoding of Time.Advances in experimental medicine and biology · 2024Review
- Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks.Scientific reports · 2023Article
- Long- and short-term history effects in a spiking network model of statistical learning.Scientific reports · 2023Article
- Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Coherent noise enables probabilistic sequence replay in spiking neuronal networks.PLoS computational biology · 2023Article
- Single spikes drive sequential propagation and routing of activity in a cortical network.eLife · 2023Article
- Creation of Neuronal Ensembles and Cell-Specific Homeostatic Plasticity through Chronic Sparse Optogenetic Stimulation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Editorial: Reproducibility in neuroscience.Frontiers in integrative neuroscience · 2023Article
- Sequence learning, prediction, and replay in networks of spiking neurons.PLoS computational biology · 2022Article
- Encoding time in neural dynamic regimes with distinct computational tradeoffs.PLoS computational biology · 2022Article
- Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals.Scientific reports · 2022Article
- Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.PLoS computational biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.
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.