Continuous Quasi-Attractors dissolve with too much - or too little - variability.

Recent research involving bats flying in long tunnels has confirmed that hippocampal place cells can be active at multiple locations, with considerable variability in place field size and peak rate. With self-organizing recurrent networks, variability implies inhomogeneity in the synaptic weights, impeding the establishment of a continuous manifold of fixed points. Are continuous attractor neural networks still valid models for understanding spatial memory in the hippocampus, given such variability? Here, we ask what are the noise limits, in terms of an experimentally inspired parametrization of the irregularity of a single map, beyond which the notion of continuous attractor is no longer relevant.

Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.

Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data.

Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity.

Continuous attractor neural networks (CANN) form an appealing conceptual model for the storage of information in the brain. However a drawback of CANN is that they require finely tuned interactions. We here study the effect of quenched noise in the interactions on the coding of positional information within CANN.

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment.

We present here an approach to protein design that combines (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learn a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training.

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data.

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear.