Using a computational approach for designing object recognition tasks is beneficial to highlight the different strategies among different species.

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

Context-based object recognition causally relies on both scene- and object-selective cortex, with scene-selective cortex generating expectations (at 160-200 ms after onset) that disambiguate object representations in object-selective cortex (at 260-300 ms after onset).

Combining 7 Tesla fMRI and MEG data collected during a challenging visual recognition task revealed distinct neural representational formats in ventral visual and frontoparietal regions, and the emergence of recognition-related signals prior to category-related information.

Human-like color categories emerge in a Deep Neural Network trained only on object recognition.

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.

Like primates, the rat brain areas thought to be involved in visual object recognition are arranged in a hierarchy.

Complementary neural codes in frontal and visual cortex support a role for feedback signals in the representation and recognition of partially occluded objects.

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.

Goal-directed interaction with objects and spatial navigation are subserved by the perirhinal-lateral entorhinal networks and the postrhinal-medial entorhinal networks, respectively, with action-based functional differentiation more strongly represented in the entorhinal cortex than its upstream.