Mounting evidence suggests that “core object recognition,” the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains little-understood. Here we review evidence ranging from individual neurons, to neuronal populations, to behavior, to computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical sub-networks with a common functional goal.

Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations.

Neurons in area MT (V5) are selective for the direction of visual motion. In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells. We fit this cascade model to responses of individual MT neurons and show that it robustly predicts the separately measured responses to gratings and plaids. The model captures the full range of pattern motion selectivity found in MT. Cells that signal pattern motion are distinguished by having convergent excitatory input from V1 cells with a wide range of preferred directions, strong motion opponent suppression and a tuned normalization that may reflect suppressive input from the surround of V1 cells.