5 papers
Controlled Langevin Dynamics for Sampling of Feedforward Neural Networks Trained with Minibatches
Alessandro Zambon, Francesca Caruso, Riccardo Zecchina +1
Sampling the parameter space of artificial neural networks according to a Boltzmann distribution provides insight into the geometry of low-loss solutions and offers an alternative…
Sampling the space of solutions of an artificial neural network
Alessandro Zambon, Enrico M. Malatesta, Guido Tiana +1
The weight space of an artificial neural network can be systematically explored using tools from statistical mechanics. We employ a combination of a hybrid Monte Carlo algorithm wh…
Impact of dendritic non-linearities on the computational capabilities of neurons
Clarissa Lauditi, Enrico M. Malatesta, Fabrizio Pittorino +3
How neurons integrate the myriad synaptic inputs scattered across their dendrites is a fundamental question in neuroscience. Multiple neurophysiological experiments have shown that…
Structure of the space of folding protein sequences defined by large language models
A. Zambon, R. Zecchina, G. Tiana
Proteins populate a manifold in the high-dimensional sequence space whose geometrical structure guides their natural evolution. Leveraging recently-developed structure prediction t…
Native state of natural proteins optimises local entropy
Matteo Negri, Guido Tiana, Riccardo Zecchina
The differing ability of polypeptide conformations to act as the native state of proteins has long been rationalized in terms of differing kinetic accessibility or thermodynamic st…