Competitive Hebbian learning through spike-timing-dependent synaptic plasticity

By Sen Song, Kenneth D. Miller, L. F. Abbott. 2000

2,914 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering, Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Wulfram Gerstner
2. Friedemann Zenke
3. D. Feldman
4. Matthieu Gilson
5. Niraj S. Desai
6. Pierre Yger
7. Xiao-Jing Wang
8. Anthony N. Burkitt

In Cognitive Neuroscience

1. Wulfram Gerstner
2. Friedemann Zenke
3. D. Feldman
4. Xiao-Jing Wang
5. Pierre Yger
6. Matthieu Gilson
7. Stefano Fusi
8. Anthony N. Burkitt

In Electrical and Electronic Engineering

1. Wulfram Gerstner
2. Friedemann Zenke
3. Pierre Yger
4. Matthieu Gilson
5. Stefano Fusi
6. Niraj S. Desai
7. D. Feldman
8. Timothée Masquelier

In Neuroscience

1. Wulfram Gerstner
2. E. D. Di Paolo
3. Matthieu Gilson
4. Anthony N. Burkitt
5. Maxim Bazhenov
6. J. Urgen Schmidhuber
7. D. Feldman
8. Andreas Knoblauch

Citations in the corpus

• Continual Lifelong Learning with Neural Networks: A Review (2018, background): “systems is typically achieved by augmenting Hebbian plasticity with additional constraints such as upper limits on the individual synaptic weights or average neural activity (Miller and MacKay, 1994; Song et al., 2000). Homeostatic mechanisms of plasticity include synaptic scaling and metaplasticity”
• Deep learning in neural networks: An overview (2014, methodology): “Many computational models of such spiking neurons have been proposed and analyzed (e.g., Gerstner and van Hemmen, 1992; Zipser et al., 1993; Stemmler, 1996; Tsodyks et al., 1996; Maex and Orban, 1996; Maass, 1996, 1997; Kistler et al., 1997; Amit and Brunel, 1997; Kempter et al., 1999; Song et al., ”
• Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks (2020, background): “It has been shown that STDP results in balanced activity, where all neurons fire in equal proportions (Song et al. (2000)).”
• Memory retention and spike-timing-dependent plasticity. (2009, methodology): “One STDP model updates the weights in a manner that is independent of the current synaptic weight (nSTDP) (Song et al. 2000), whereas the other model updates the weights in a way that does depend on the synaptic weight (wSTDP) (Rubin et al. 2001; van Rossum et al. 2000).”
• A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons (2011, background): “In recent years, the development of large-scale networks with spiking neurons [1] and adaptive synapses based on spike-timing dependent plasticity (STDP) [2] has suggested new avenues of exploration for brain-like cognitive computing.”
• Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, methodology): “Classic pair-based STDP models (Gerstner et al., 1996; Kempter et al., 1999; Song et al., 2000; van Rossum et al., 2000; Rubin et al., 2001) are further examples of the general theoretical framework of Equation (1) and so are some models of structural plasticity (Helias et al., 2008; Deger et al.,…”
• Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning (2001, background): “Some preliminary arguments as to why intrinsic normalization occurs in spike-based learning rules have been given (Gerstner et al., 1998; Kempter et al., 1999a; Song et al., 2000).”
• Spike timing-dependent plasticity: from synapse to perception. (2006, background): “This form of activity-dependent LTP/LTD is now referred to as spike timing-dependent plasticity (STDP) (99).”