5 papers
Temporal Complexity and Self-Organization in an Exponential Dense Associative Memory Model
Marco Cafiso, Paolo Paradisi
Dense Associative Memory (DAM) models generalize the classical Hopfield model by incorporating n-body or exponential interactions that greatly enhance storage capacity. While the c…
Criticality of a Stochastic Dense Associative Memory Model with Exponential Interaction Function
Marco Cafiso, Paolo Paradisi
The Hopfield network (HN) is a classical model of associative memory with stored patterns encoded as minima of an energy function shaped by a Hebbian learning rule. Dense Associati…
Complexity of Activity Patterns in a Bio-Inspired Hopfield-Type Network in Different Topologies
Marco Cafiso, Paolo Paradisi
Neural network models capable of storing memory have been extensively studied in computer science and computational neuroscience. The Hopfield network is a prototypical example of…
Robustness of complexity estimation in event-driven signals against accuracy of event detection method
Marco Cafiso, Paolo Paradisi
Complexity has gained recent attention in machine learning for its ability to extract synthetic information from large datasets. Complex dynamical systems are characterized by temp…
Temporal Complexity of a Hopfield-Type Neural Model in Random and Scale-Free Graphs
Marco Cafiso, Paolo Paradisi
The Hopfield network model and its generalizations were introduced as a model of associative, or content-addressable, memory. They were widely investigated both as an unsupervised…