High-level visual brain regions contain subareas in which neurons appear to respond more strongly to examples of a particular semantic category, like faces or bodies, rather than objects. However, recent work has shown that while this finding holds on average, some out-of-category stimuli also activate neurons in these regions. This may be due to visual features common among the preferred class also being present in other images. Here, we propose a deep-learning-based approach for visualizing these features. For each neuron, we identify relevant visual features driving its selectivity by modelling responses to images based on latent activations of a deep neural network. Given an out-of-category image which strongly activates the neuron, our method first identifies a reference image from the preferred category yielding a similar feature activation pattern. We then backpropagate latent activations of both images to the pixel level, while enhancing the identified shared dimensions and attenuating non-shared features. The procedure highlights image regions containing shared features driving responses of the model neuron. We apply the algorithm to novel recordings from body-selective regions in macaque IT cortex in order to understand why some images of objects excite these neurons. Visualizations reveal object parts which resemble parts of a macaque body, shedding light on neural preference of these objects.

The brain has an amazing capacity for recognizing faces. It can identify a face in a few thousandths of a second, form a first impression of its owner and retain the memory for decades. Central to these abilities is a longstanding puzzle: how the image of a face is encoded by the brain. Two Caltech biologists, Le Chang and Doris Y. Tsao, reported in Thursday's issue of Cell that they have deciphered the code of how faces are recognized. Their experiments were based on electrical recordings from face cells, the name given to neurons that respond with a burst of electric signals when an image of a face is presented to the retina.

The brain has an amazing capacity for recognizing faces. It can identify a face in a few thousandths of a second, form a first impression of its owner and retain the memory for decades. Central to these abilities is a longstanding puzzle: how the image of a face is encoded by the brain. Two Caltech biologists, Le Chang and Doris Y. Tsao, reported in Thursday's issue of Cell that they have deciphered the code of how faces are recognized. Their experiments were based on electrical recordings from face cells, the name given to neurons that respond with a burst of electric signals when an image of a face is presented to the retina.

Just 200 face cells are required to identify a face, the biologists say. After discovering how its features are encoded, the biologists were able to reconstruct the faces a monkey was looking at just by monitoring the pattern in which its face cells were firing. The finding needs to be confirmed in other laboratories. But, if correct, it could help understand how the brain encodes all seen objects, as well as suggesting new approaches to artificial vision. "Cracking the code for faces would definitely be a big deal," said Brad Duchaine, an expert on face recognition at Dartmouth.

When we look at a selection of faces, our brains can single out the familiar ones with no effort at all. This smooth process comes so naturally that most people never give it a second thought. But someone who does give this phenomenon a second thought is Doris Tsao, a professor of biology and biological engineering at the California Institute of Technology in Pasadena. Over recent years, Prof. Tsao has conducted a range of experiments that have attempted to get to the bottom of facial perception. In earlier studies, Prof. Tsao and her colleagues used functional MRI scans to search for relevant brain areas in humans and other primates.

The code used by the brain to recognise faces may finally have been cracked by a mind-reading computer. Scientists programmed an algorithm that is able to recreate images of faces shown to monkeys in astonishing detail by measuring brain activity. The findings seem to solve one of the most difficult problems in neuroscience - and researchers say the explanation is surprisingly simple. The discovery could lead to software in the future that allows machines to probe further into our minds. Researchers have made a computer algorithm that is able to accurately recreate faces seen by macaque monkeys by measuring their brain activity.

Scientists in the US have accurately reconstructed images of human faces by monitoring the responses of monkey brain cells. The brains of primates can resolve different faces with remarkable speed and reliability, but the underlying mechanisms are not fully understood. The researchers showed pictures of human faces to macaques and then recorded patterns of brain activity. The work could inspire new facial recognition algorithms, they report. In earlier investigations, Professor Doris Tsao from the California Institute of Technology (Caltech) and colleagues had used functional magnetic resonance imaging (fMRI) in humans and other primates to work out which areas of the brain were responsible for identifying faces.