29 citations · 63 across the 3 of their papers we have counts for
5 papers
Learn to Synchronize, Synchronize to Learn
Pietro Verzelli, Cesare Alippi, Lorenzo Livi
In recent years, the machine learning community has seen a continuous growing interest in research aimed at investigating dynamical aspects of both training procedures and machine…
Input-to-State Representation in linear reservoirs dynamics
Pietro Verzelli, Cesare Alippi, Lorenzo Livi +1
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is…
A study of dependency features of spike trains through copulas
Pietro Verzelli, Laura Sacerdote
Simultaneous recordings from many neurons hide important information and the connections characterizing the network remain generally undiscovered despite the progresses of statisti…
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
Pietro Verzelli, Cesare Alippi, Lorenzo Livi
Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure. These networks are know…
A characterization of the Edge of Criticality in Binary Echo State Networks
Pietro Verzelli, Lorenzo Livi, Cesare Alippi
Echo State Networks (ESNs) are simplified recurrent neural network models composed of a reservoir and a linear, trainable readout layer. The reservoir is tunable by some hyper-para…