A palimpsest memory based on an incremental Bayesian learning rule

By Anders Sandberg, Anders Lansner, Karl Magnus Petersson, Örjan Ekeberg. 2000

32 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. Andreas Knoblauch
2. Friedrich T. Sommer
3. Edgar Körner
4. Ursula Körner

In Cognitive Neuroscience

1. Andreas Knoblauch
2. Friedrich T. Sommer
3. Edgar Körner
4. Ursula Körner
5. Günther Palm

In Electrical and Electronic Engineering

1. Andreas Knoblauch
2. Friedrich T. Sommer
3. Edgar Körner
4. Ursula Körner
5. Günther Palm

Citations in the corpus

• Optimal synaptic learning in non-linear associative memory (2010, background): “…learning models of the Hopfield type [30], [58], [32], [33], [53], [34], [35], [36], [37], simple non-linear learning models of the Willshaw type [24], [23], [25], [59], [26], [28], [18], [29], and BCPNN-type models employing suboptimal Bayesian heuristics [38], [39], [40], [41], [42], [43], [6].”
• Neural Associative Memory with Optimal Bayesian Learning (2011, background): “Another future direction will be to investigate more closely the biological relevance of Bayesian learning by implementing more realistic network models that include spikes, forgetful synapses, and inhibitory circuits (Sandberg et al., 2000; Fusi, Drew, & Abbott, 2005; Markram et al., 2004).”