Associative Memory with Dynamic Synapses

By Lovorka Pantic, J. Torres, Hilbert J. Kappen, S. Gielen. 2002

108 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Yutian Chen
2. Max Welling
3. Anders Lansner
4. Yoshua Bengio
5. Sergey Levine
6. Francis R. Bach
7. Bernhard Schölkopf
8. Pieter Abbeel

In Cognitive Neuroscience

1. Max Welling
2. Anders Lansner
3. Andreas Knoblauch
4. Geoffrey E. Hinton

In Electrical and Electronic Engineering

1. Max Welling
2. Yutian Chen
3. Anders Lansner

Citations in the corpus

• Statistical inference using weak chaos and infinite memory (2010, background): “Herding in Hopfield networks shows strong similarities to the relatively recently discovered dynamical weights in real neurons (Maass & Zador, 1998; Tsodyks et al., 1098; Pantic et al., 2002; Pfister et al., 2009).”
• A working memory model based on fast Hebbian learning (2003, methodology): “This adaptation model was chosen due to its simplicity and symmetry with the learning rule used, and produces the same type of attractor-switching behaviour as other cellular adaptation [7, 30, 55] and synaptic depression based models [36, 48].”
• Herding dynamical weights to learn (2009, unknown): “(Maass & Zador, 1998; Pantic et al., 2002; Levina et al., 2007).”