Simple modifications to early stages of the visual hierarchy, such as gain changes, can induce complex effects on later stages, but only gain is both necessary and sufficient to explain enhanced perception during spatial attention.

Cortical network model suggests a mechanism explaining the link between NMDAR synaptic and spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

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.

A new method for inducing, measuring and modelling visual hallucinations in healthy people reveals that hallucinations share common underlying mechanisms with normal sensory perception.

The BB model explains spatial cognition in terms of interactions between specific neuronal populations, providing a common computational framework for the human neuropsychological and in vivo animal electrophysiological literatures.

Prediction of future input explains diverse neural tuning properties in sensory cortex.

Attention reduces noise correlations through enhancing inhibitory feedback in cortical networks.

Model identification of neural encoding is an accessible system for the analysis of neural data that allows identifying and characterizing arbitrary relationships between neural activity and task-related variables such as behavior, stimuli, or internal states.

The lower-level retinotopic visual cortex of humans born without the optic chiasm comprises two independent neuronal populations and forms a versatile model for quantifying the relationship between the fMRI BOLD signal and neural response.

Experiments and modeling suggest that a mechanism involving positive and negative feedback can explain the relationship between the neural activity of socially interacting bats.