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.

Computational modeling reveals the mechanisms by which a diverse group of neurons in the brainstem generates rhythmic breathing in mammals.

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.

Organization of commissural and long propriospinal neuronal pathways defines gait expression in quadrupeds.

Theoretical ideas have a rich history in many areas of biology, and new theories and mathematical models have much to offer in the future.

Computational modeling and analysis of mouse neural population data finds that the excitation/inhibition imbalance theory of brain disorders is too limited to account for key changes in neural activity statistics.

A homopolymer-sphere model is shown to accurately reproduce the interactions that underpin selective gating of macromolecular transport into and out of the cell nucleus.

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.

A biologically plausible learning rule allows recurrent neural networks to learn nontrivial tasks, using only sparse, delayed rewards, and the neural dynamics of trained networks exhibit complex dynamics observed in animal frontal cortices.

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.