Direct in-vivo measurements in the human brain test validity of detailed computational models of trancranial electric stimulation and show that electric fields in the brain are weaker than currently assumed.
Spontaneous theta oscillations and interneuron-specific phase preferences emerge spontaneously in a full-scale model of the isolated hippocampal CA1 subfield, corroborating and extending recent experimental findings.
Leave-One-Trial-Out (LOTO) is a general, efficient and easily implementable approach for inferring trial-by-trial measures of computational model parameters in order to link these measures to neural mechanisms.
A spiking network model that examines the transformation of odor information from olfactory bulb to piriform cortex demonstrates how intrinsic cortical circuitry preserves representations of odor identity across odorant concentrations.
An unbiased model for the self-organisation of the Golgi apparatus displays either anterograde vesicular transport or cisternal maturation depending on ratios of budding, fusion and biochemical conversion rates.
Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.