Browse the search results

Computer simulations show that the firing patterns of branched touch receptors can be set in part by the organization of their sensory endings in the skin.

Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.

Computational analysis of MNase-seq data reveals that alternatively positioned nucleosomes are prevalent in both yeast and human cells and create significant heterogeneity within cell populations.

A computational model shows that natural selection can cause populations to evolve a distinctive population-level phenotype: the ability to transition between collective states in response to the environment.

Applying computational modeling to quantify threat learning processes uncovers how variations in these conserved learning processes relate to anxiety severity and the neuroanatomical substrates moderating these associations.

A combination of computational approaches identifies a novel allosteric hotspot in cystic fibrosis transmembrane conductance regulator.

Radial plant growth produces large parts of terrestrial biomass and can be computationally simulated with the help of an instructive framework of intercellular communication loops.

A set of canonical computational principles implemented in a simple feedforward neural network naturally gives rise to the network's sensitivity to numerosity and its illusory effects, providing an explanation for the ubiquity of the number sense in the animal kingdom.

A computational method has been developed for predicting the cell-type-specific binding of transcription factors to nucleosomes, and involves integration of ChIP-seq, MNase-seq, and DNase-seq data with details of nucleosome structure.

ON and OFF visual motion is computed with the same algorithm despite differences in circuit architecture.