The relationship between perception and inference, as postulated by Helmholtz in the 19th century, is paralleled in modern machine learning by generative models like Variational Autoencoders (VAEs) and their hierarchical variants. Here, we evaluate the role of hierarchical inference and its alignment with brain function in the domain of motion perception. We first introduce a novel synthetic data framework, Retinal Optic Flow Learning (ROFL), which enables control over motion statistics and their causes. We then present a new hierarchical VAE and test it against alternative models on two downstream tasks: (i) predicting ground truth causes of retinal optic flow (e.g., self-motion); and (ii) predicting the responses of neurons in the motion processing pathway of primates. We manipulate the model architectures (hierarchical versus non-hierarchical), loss functions, and the causal structure of the motion stimuli.

The relationship between perception and inference, as postulated by Helmholtz in the 19th century, is paralleled in modern machine learning by generative models like Variational Autoencoders (VAEs) and their hierarchical variants. Here, we evaluate the role of hierarchical inference and its alignment with brain function in the domain of motion perception. We first introduce a novel synthetic data framework, Retinal Optic Flow Learning (ROFL), which enables control over motion statistics and their causes. We then present a new hierarchical VAE and test it against alternative models on two downstream tasks: (i) predicting ground truth causes of retinal optic flow (e.g., self-motion); and (ii) predicting the responses of neurons in the motion processing pathway of primates. We manipulate the model architectures (hierarchical versus non-hierarchical), loss functions, and the causal structure of the motion stimuli.

The world's first high-resolution 3D image of a monkey brain has been revealed, in a breakthrough that could pave the way for treatments for human diseases including Parkinson's. A detailed map of a complete macaque monkey brain was created using fluorescent imaging techniques by a team from the Chinese Academy of Sciences in Beijing. The team used a new technique to show how nerve cells are organised and connected within the monkey brain at a'micron resolution'. The human brain comprises nearly a hundred billion nerve cells with delicate and complex connections, and while up to 17 times larger than that of a macaque, it is similar enough for comparisons to be made between the two, researchers claim. Until now, a mouse brain was the largest to be mapped, taking days to create a complete 3D image, but the new technique made it possible to move up to a macaque brain, which is about 200 times larger in volume than that of a mouse.

Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there's a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for artificial intelligence developers, such as those improving self-driving car navigation. While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child's play for primates such as humans. In findings published in Nature Neuroscience, McGovern Institute investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

Apes are our closest animal relatives. We share 99.5 percent of our DNA material with them, but the 0.5 percent we do not share creates the vast differences between the two species. The differences are seen in physical features and intelligence. Most regions in our brain are, on a molecular level, very similar to primate brains. The various regions of the human brain have a similar structure and function as our primate relatives.

You could argue that artificial intelligence (AI) got started many years ago with a grand challenge. That, of course, was Alan Turing's "Imitation Game," which he presented in his seminal 1950 paper "Computing Machinery and Intelligence." Essentially, it involved building machines that can behave indistinguishably from the way a thinker behaves. In the example that Turing provides, that behavior is entirely focused on human (or humanlike) conversation. In many ways, that grand challenge is already history.

Familiarity alters face recognition: Familiar faces are recognized more accurately than unfamiliar ones and under difficult viewing conditions when unfamiliar face recognition fails. The neural basis for this fundamental difference remains unknown. Using whole-brain functional magnetic resonance imaging, we found that personally familiar faces engage the macaque face-processing network more than unfamiliar faces. Familiar faces also recruited two hitherto unknown face areas at anatomically conserved locations within the perirhinal cortex and the temporal pole. These two areas, but not the core face-processing network, responded to familiar faces emerging from a blur with a characteristic nonlinear surge, akin to the abruptness of familiar face recognition.

Primate cognition requires interaction processing. Interactions can reveal otherwise hidden properties of intentional agents, such as thoughts and feelings, and of inanimate objects, such as mass and material. Where and how interaction analyses are implemented in the brain is unknown. Using whole-brain functional magnetic resonance imaging in macaque monkeys, we discovered a network centered in the medial and ventrolateral prefrontal cortex that is exclusively engaged in social interaction analysis. Exclusivity of specialization was found for no other function anywhere in the brain.

For decades, neuroscientists have been trying to design computer networks that can mimic visual skills such as recognizing objects, which the human brain does very accurately and quickly. Until now, no computer model has been able to match the primate brain at visual object recognition during a brief glance. However, a new study from MIT neuroscientists has found that one of the latest generation of these so-called "deep neural networks" matches the primate brain. Because these networks are based on neuroscientists' current understanding of how the brain performs object recognition, the success of the latest networks suggest that neuroscientists have a fairly accurate grasp of how object recognition works, says James DiCarlo, a professor of neuroscience and head of MIT's Department of Brain and Cognitive Sciences and the senior author of a paper describing the study in the Dec. 18 issue of the journal PLoS Computational Biology. "The fact that the models predict the neural responses and the distances of objects in neural population space shows that these models encapsulate our current best understanding as to what is going on in this previously mysterious portion of the brain," says DiCarlo, who is also a member of MIT's McGovern Institute for Brain Research.

Scientists have shown how the brain anticipates all of the new situations that it may encounter in a lifetime by creating a special kind of neural network that is "pre-adapted" to face any eventuality. This emerges from a new neuroscience study published in PLOS Computational Biology. Enel et al at the INSERM in France investigate one of the most noteworthy properties of primate behavior, its diversity and adaptability. Human and non-human primates can learn an astonishing variety of novel behaviors that could not have been directly anticipated by evolution--we now understand that this ability to cope with new situations is due to the "pre-adapted" nature of the primate brain. This study shows that this seemingly miraculous pre-adaptation comes from connections between neurons that form recurrent loops where inputs can rebound and mix in the network, like waves in a pond, thus called "reservoir" computing.