Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence

By Matthieu Gilson, Anthony N. Burkitt, David B. Grayden, D. A. Thomas, J. van Hemmen. 2010

41 citations recorded by the source corpus.

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

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. Friedemann Zenke
2. Wulfram Gerstner
3. Surya Ganguli
4. Markus Diesmann
5. Abigail Morrison
6. Maoz Shamir
7. Yotam Luz
8. Susanne Kunkel

In Cognitive Neuroscience

1. Friedemann Zenke
2. Wulfram Gerstner
3. Surya Ganguli
4. Abigail Morrison
5. Markus Diesmann
6. Maoz Shamir
7. Yotam Luz
8. Susanne Kunkel

In Electrical and Electronic Engineering

1. Friedemann Zenke
2. Wulfram Gerstner
3. Surya Ganguli
4. Abigail Morrison
5. Markus Diesmann
6. Susanne Kunkel
7. Maoz Shamir
8. Yotam Luz

Citations in the corpus

• The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “Such a stabilizing weight dependence is advantageous because it can lead to more plausible unimodal weight distributions as observed empirically [72, 74].”
• Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity (2012, background): “Recent theoretical studies have also managed to investigated learning of recurrent excitation [44–47].”
• Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks (2011, background): “Several forms of synaptic pathway organization have been identified and mathematically analyzed for recurrent networks under the assumption that they receive input from more than one pool of correlated inputs (e.g., Gilson et al., 2009b,c; see Gilson et al., 2010 for a thorough review).”