Electronic supplementary material: Hebbian plasticity requires compensatory processes on multiple timescales

By Friedemann Zenke, Wulfram Gerstner. 2016

How researchers see this paper

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

1. German Ignacio Parisi
2. Stefan Wermter
3. Jun Tani
4. Cornelius Weber
5. Luiza Mici
6. Lawrence C. Udeigwe
7. G. Bard Ermentrout
8. Paul W. Munro

Citations in the corpus

• Lifelong learning of human actions with deep neural network self-organization (2017, background): “Implausible mechanisms of Hebbian learning inherited by SOMlike models have been extended with additional stabilization and competition processes for biologically plausible experience-driven learning (e.g. Zenke & Gerstner, 2017; Zenke, Gerstner, & Ganguli, 2017).”
• Emergent Dynamical Properties of the BCM Learning Rule (2017, background): “Zenke and Gerstner [39] suggest that learning and memory use an interplay of both forms of homeostasis; while fast homeostatic control mechanisms help maintain the stability of synaptic plasticity, slower ones are important for fine-tuning neural circuits.”