ArticlePloS one2014
A spiking network model of decision making employing rewarded STDP.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation.bioRxiv : the preprint server for biology · 2026Article
- Heterosynaptic plasticity-induced modulation of synapses.The journal of physiological sciences : JPS · 2023Review
- Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation.PLoS computational biology · 2022Article
- Inhibitory Metaplasticity in Juvenile Stressed Rats Restores Associative Memory in Adulthood by Regulating Epigenetic Complex G9a/GLP.The international journal of neuropsychopharmacology · 2022Article
- A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks.Frontiers in neurorobotics · 2018Review
- Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.PLoS computational biology · 2017Article
- Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery.Frontiers in neurology · 2017Article
- Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016Article
- Homeostatic role of heterosynaptic plasticity: models and experiments.Frontiers in computational neuroscience · 2015Review
- Heterosynaptic plasticity: multiple mechanisms and multiple roles.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2014Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Reward-modulated spike timing dependent plasticity (STDP) combines unsupervised STDP with a reinforcement signal that modulates synaptic changes. It was proposed as a learning rule capable of solving the distal reward problem in reinforcement learning. Nonetheless, performance and limitations of this learning mechanism have yet to be tested for its ability to solve biological problems. In our work, rewarded STDP was implemented to model foraging behavior in a simulated environment. Over the course of training the network of spiking neurons developed the capability of producing highly successful decision-making. The network performance remained stable even after significant perturbations of synaptic structure. Rewarded STDP alone was insufficient to learn effective decision making due to the difficulty maintaining homeostatic equilibrium of synaptic weights and the development of local performance maxima. Our study predicts that successful learning requires stabilizing mechanisms that allow neurons to balance their input and output synapses as well as synaptic noise.
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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.