A glance at an object is often sufficient to recognize it and recover fine details of its shape and appearance, even under highly variable viewpoint and lighting conditions. How can vision be so rich, but at the same time fast? The analysisby- synthesis approach to vision offers an account of the richness of our percepts, but it is typically considered too slow to explain perception in the brain. Here we propose a version of analysis-by-synthesis in the spirit of the Helmholtz machine (Dayan, Hinton, Neal, & Zemel, 1995) that can be implemented efficiently, by combining a generative model based on a realistic 3D computer graphics engine with a recognition model based on a deep convolutional network. The recognition model initializes inference in the generative model, which is then refined by brief runs of MCMC. We test this approach in the domain of face recognition and show that it meets several challenging desiderata: it can reconstruct the approximate shape and texture of a novel face from a single view, at a level indistinguishable to humans; it accounts quantitatively for human behavior in “hard” recognition tasks that foil conventional machine systems; and it qualitatively matches neural responses in a network of face-selective brain areas. Comparison to other models provides insights to the success of our model.

From a quick glance or the touch of an object, our brains map sensory signals to scenes composed of rich anddetailed shapes and surfaces. Unlike the standard approaches to perception, we argue that this mapping draws on internalcausal and compositional models of the physical world and these internal models underlie the generalization capacity of humanperception. Here, we present a generative model of visual and multisensory perception in which the latent variables encodeintrinsic (e.g., shape) and extrinsic (e.g., occlusion) object properties. Latent variables are inputs to causal models that outputsense-specific signals. We present a recognition network that performs efficient inference in the generative model, computingat a speed similar to online perception. We show that our model, but not alternatives, can account for human performance in anoccluded face matching task and in a visual-to-haptic face matching task.

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.