ArticlePLoS computational biology2016
Learning Universal Computations with Spikes.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Combining sampling and attractor dynamics in spiking models of head direction systems.PLoS computational biology · 2026Article
- Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks.Nature communications · 2025Article
- Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates.PLoS computational biology · 2025Article
- Cooperative coding of continuous variables in networks with sparsity constraint.PLoS computational biology · 2025Article
- Article
- Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.bioRxiv : the preprint server for biology · 2025Article
- Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation.Frontiers in neuroscience · 2024Article
- Article
- Volitional Generation of Reproducible, Efficient Temporal Patterns.Brain sciences · 2022Article
- Article
- Intrinsic bursts facilitate learning of Lévy flight movements in recurrent neural network models.Scientific reports · 2022Article
- Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals.Scientific reports · 2022Article
- Learning the Synaptic and Intrinsic Membrane Dynamics Underlying Working Memory in Spiking Neural Network Models.Neural computation · 2021Article
- Efficient and robust coding in heterogeneous recurrent networks.PLoS computational biology · 2021Article
- Emerging Artificial Neuron Devices for Probabilistic Computing.Frontiers in neuroscience · 2021Review
- Learning to represent signals spike by spike.PLoS computational biology · 2020Article
- Simple framework for constructing functional spiking recurrent neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits.PLoS computational biology · 2019Article
- Learning recurrent dynamics in spiking networks.eLife · 2018Article
- SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.Neural computation · 2018Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.
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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.