Computational modeling of cognitive and neuroscience data is an insightful and powerful tool, but has many potential pitfalls that can be avoided by following simple guidelines.
A computational model of fission yeast mitosis can interrogate mechanisms required for successful mitosis, the origin of spindle length fluctuations, and spindle force balance during assembly.
A comprehensive, data-driven and interpretable nonlinear computational modeling framework based on deep neural networks uncovers different nonlinear transformations of speech signal in the human auditory cortex.
Attenuated anticipatory activity in ventromedial prefrontal cortex is modulated by dopamine D1 receptor density in nucleus accumbens, and accounts for impaired probabilistic reward learning in older adults.
The ion channel genealogy resource is a comprehensive and intuitive comparison tool for ion channel models and experimental data, helping to visualize their similarity and function to facilitate better experimentally-constrained modeling.
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.
A new computational model of brainstem control of locomotor speed and gait was developed to reproduce and explain recent experimental data and propose predictions for subsequent experimental testing.
A novel computation tool for microbial community modeling predicts the evolution and diversification of E. coli in laboratory evolution experiments and gives insight into the underlying metabolic processes.
The combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and optimize small sets of antigen variants for efficient experimental localization of epitopes.
