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Found 9 papers, 3 papers with code
Transition paths in Potts-like energy landscapes: general properties and application to protein sequence models
We study transition paths in energy landscapes over multi-categorical Potts configurations using the mean-field approach introduced by Mauri et al., {\em Phys Rev Lett 130, 158402 (2023)}.
Mutational paths with sequence-based models of proteins: from sampling to mean-field characterisation
Identifying and characterizing mutational paths is an important issue in evolutionary biology and in bioengineering.
Stable cognitive maps for Path Integration emerge from fusing visual and proprioceptive sensors
Spatial navigation in biological agents relies on the interplay between external (visual, olfactory, auditory, $\dots$) and proprioceptive (motor commands, linear and angular velocity, $\dots$) signals.
Barriers and Dynamical Paths in Alternating Gibbs Sampling of Restricted Boltzmann Machines
Restricted Boltzmann Machines (RBM) are bi-layer neural networks used for the unsupervised learning of model distributions from data.
Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural Networks
We study the learning dynamics and the representations emerging in Recurrent Neural Networks trained to integrate one or multiple temporal signals.
Survival probability and size of lineages in antibody affinity maturation
We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and initial population size.
Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
A Restricted Boltzmann Machine (RBM) is an unsupervised machine-learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features.
Learning protein constitutive motifs from sequence data
Statistical analysis of evolutionary-related protein sequences provides insights about their structure, function, and history.
Emergence of Compositional Representations in Restricted Boltzmann Machines
Extracting automatically the complex set of features composing real high-dimensional data is crucial for achieving high performance in machine--learning tasks.
