ArticlePloS one2013
Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bio-Inspired Neural Network Dynamics-Aware Reinforcement Learning for Spiking Neural Network.Biomimetics (Basel, Switzerland) · 2026Article
- Synaptic self-organization of spatio-temporal pattern selectivity.PLoS computational biology · 2023Article
- ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network.Frontiers in neuroscience · 2023Article
- ALSA: Associative Learning Based Supervised Learning Algorithm for SNN.Frontiers in neuroscience · 2022Article
- [A review of brain-like spiking neural network and its neuromorphic chip research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks.Frontiers in computational neuroscience · 2021Article
- Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.Computational intelligence and neuroscience · 2021Article
- Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations.Materials (Basel, Switzerland) · 2019Review
- Odor Recognition with a Spiking Neural Network for Bioelectronic Nose.Sensors (Basel, Switzerland) · 2019Article
- Computing of temporal information in spiking neural networks with ReRAM synapses.Faraday discussions · 2019Article
- First Error-Based Supervised Learning Algorithm for Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons.Frontiers in neuroscience · 2019Article
- Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.Science advances · 2018Article
- Hough Transform Implementation For Event-Based Systems: Concepts and Challenges.Frontiers in computational neuroscience · 2018Article
- A Spiking Neural Network Framework for Robust Sound Classification.Frontiers in neuroscience · 2018Article
- A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP.Frontiers in neuroscience · 2018Article
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- Implementing Signature Neural Networks with Spiking Neurons.Frontiers in computational neuroscience · 2016Article
- Article
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4 authors.
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Abstract
A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.
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