3 papers
q-bio.NC2025
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…
cond-mat.dis-nn2024
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…
physics.bio-ph2023
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…