The superior colliculus reveals hallmarks of sophisticated visual computation, including selectivity, invariance, and stimulus-specific habituation to behaviorally relevant stimuli.

Single units in a deep convolutional neural network trained for image classification develop shape selectivity that is similar to that found in the primate visual cortex.

A large variety of spatial representations implied in rodent navigation could arise robustly and rapidly from inputs with a weak spatial structure, by an interaction of excitatory and inhibitory synaptic plasticity.

Neural sensory representations impose an inductive bias over the space of learning tasks, allowing some tasks to be learned by a downstream neuron more sample-efficiently than others.

Neural circuits in weakly electric fish perform a set of computations to allow natural communication signals to be perceived independently of their context.

High-level visual cortex and leading neural network models of the visual system retain information about multiple visual scene variables in independent, non-interfering dimensions of their population codes.

A new data analysis tool provides a concise way of visualizing neural data that summarizes all the relevant features of the population response in a single figure.

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.

Neuronal recordings from rat visual cortex reveal an object-processing pathway, along which neuronal representations become increasingly capable of supporting recognition of visual objects in spite of variation in their appearance.

Temporal continuity-induced plasticity in individual neurons of inferior temporal cortex builds neural representations that underlie robust core object recognition behavior.