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 normalization model is shown to predict responses to multiple objects across changes in the attentional state in the visual cortex, providing evidence for the role of normalization as a fundamental operation in the human brain.

Inhibitory circuits in the olfactory bulb can amplify or suppress sensory inputs over a wide range of intensities to generate robust mitral cell output.

Challenging a popular theory in neuroeconomics, a computational cognitive study provides evidence against divisive normalization, a supposedly canonical neural computation, in favor of an alternative account, range normalization, in the context of value learning.

A spatially-tuned normalization model accounts for neuronal responses to attended or unattended stimuli that are presented inside the classical receptive field or the surround, and explains various other observations.

Spatial suppression during motion perception reflects reduced neural response magnitudes in visual areas but is not primarily driven by neural inhibition.

Divisive normalisation effects on decision making caused by distractor options can be reduced by stimulating the parietal cortex using transcranial magnetic stimulation.

Computational and theoretical analyses offer novel and unexpected insight into how complex, naturally occurring odor mixtures are parsed and normalized at the very first stage of olfaction.

A medaka mutant revealed that Wnt11 promotes formation of uniquely large protrusions from non-myogenic dorsal dermomyotome cells, which guide the epaxial myotome dorsally to achieve the coverage of the neural tube.

The value of any choice is not static but dynamically changes as a function of the context of the alternatives even if they are seemingly irrelevant.