85 citations · 114 across the 5 of their papers we have counts for
6 papers
Investigating the generative dynamics of energy-based neural networks
Lorenzo Tausani, Alberto Testolin, Marco Zorzi
Generative neural networks can produce data samples according to the statistical properties of their training distribution. This feature can be used to test modern computational ne…
A developmental approach for training deep belief networks
Matteo Zambra, Alberto Testolin, Marco Zorzi
Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data. DBNs had a catalytic effect in t…
On the difficulty of learning and predicting the long-term dynamics of bouncing objects
Alberto Cenzato, Alberto Testolin, Marco Zorzi
The ability to accurately predict the surrounding environment is a foundational principle of intelligence in biological and artificial agents. In recent years, a variety of approac…
Perception of visual numerosity in humans and machines
Alberto Testolin, Serena Dolfi, Mathijs Rochus +1
Numerosity perception is foundational to mathematical learning, but its computational bases are strongly debated. Some investigators argue that humans are endowed with a specialize…
Brain Controllability: not a slam dunk yet
Samir Suweis, Chengyi Tu, Rodrigo P. Rocha +3
In our recent article (Tu et al., Warnings and caveats in brain controllability, arXiv:1705.08261) we provided quantitative evidence to show that there are warnings and caveats in…
Warnings and Caveats in Brain Controllability
Chengyi Tu, Rodrigo P. Rocha, Maurizio Corbetta +3
In this work we challenge the main conclusions of Gu et al work (Controllability of structural brain networks. Nature communications 6, 8414, doi:10.1038/ncomms9414, 2015) on brain…