21 citations · 21 across the 5 of their papers we have counts for
5 papers
Neural Learning Rules from Associative Networks Theory
Daniele Lotito
Associative networks theory is increasingly providing tools to interpret update rules of artificial neural networks. At the same time, deriving neural learning rules from a solid t…
Learning in Associative Networks through Pavlovian Dynamics
Daniele Lotito, Miriam Aquaro, Chiara Marullo
Hebbian learning theory is rooted in Pavlov's Classical Conditioning. While mathematical models of the former have been proposed and studied in the past decades, especially in spin…
Inverse modeling of time-delayed interactions via the dynamic-entropy formalism
Elena Agliari, Francesco Alemanno, Adriano Barra +3
Although instantaneous interactions are unphysical, a large variety of maximum entropy statistical inference methods match the model-inferred and the empirically-measured equal-tim…
Dense Hebbian neural networks: a replica symmetric picture of supervised learning
Elena Agliari, Linda Albanese, Francesco Alemanno +5
We consider dense, associative neural-networks trained by a teacher (i.e., with supervision) and we investigate their computational capabilities analytically, via statistical-mecha…
Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning
Elena Agliari, Linda Albanese, Francesco Alemanno +5
We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, a…