Bathing can change the primates' parasites and gut microbes. Breakthroughs, discoveries, and DIY tips sent six days a week. When the temperatures plunge and snow falls, it's understandable to envy a snow monkey soaking in a steaming hot spring. Officially called Japanese macaques (), the primates are well known for taking advantage of the warm waters during snowy winters. While the hot water helps keep their bodies toasty in parts of Japan that can be covered with feet of snow for months at a time, there may be more to this unique behavior than meets the eye.

The macaques were status symbols all the way from India. Breakthroughs, discoveries, and DIY tips sent every weekday. Elites in Ancient Rome went to great lengths to advertise their status and wealth. Based on recent archaeological excavations in Egypt, at least some high-ranking military officials even showed off with their choice of pets. In the, researchers at Poland's University of Warsaw described a nearly 2,000-year-old animal cemetery in the Egyptian port city of Berenike that includes the remains of multiple macaque monkeys .

During natural evolution, the primary visual cortex (V1) of lower mammals typically forms salt-and-pepper organizations, while higher mammals and primates develop pinwheel structures with distinct topological properties.

During natural evolution, the primary visual cortex (V1) of lower mammals typically forms salt-and-pepper organizations, while higher mammals and primates develop pinwheel structures with distinct topological properties.

Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representation Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream.

Second row: images reconstructed by AI based on brain recordings from a macaque. Artificial intelligence systems can now create remarkably accurate reconstructions of what someone is looking at based on recordings of their brain activity. These reconstructed images are greatly improved when the AI learns which parts of the brain to pay attention to. "As far as I know, these are the closest, most accurate reconstructions," says Umut Güçlü at Radboud University in the Netherlands. How this moment for AI will change society forever (and how it won't) Güçlü's team is one of several around the world using AI systems to work out what animals or people are seeing from brain recordings and scans. In one previous study, his team used a functional MRI (fMRI) scanner to record the brain activity of three people as they were shown a series of photographs.

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research offered new methodological perspectives through automatization of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identifications done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching a 82.2% accuracy and (ii) the creation of an individual recognizer for K{\=o}jima island macaques population (YOLOv8n model), reaching a 83% accuracy. We also created a K{\=o}jima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this innovative approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.

Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representation Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream.

Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast variations. In this work, we propose a deep learning method with a self-supervised loss function that takes anatomy-based constraints into account for accurate segmentation of fiber bundles on the tracer sections from macaque brains. Also, given the limited availability of manual labels, we use a semi-supervised training technique for efficiently using unlabeled data to improve the performance, and location constraints for further reduction of false positives. Evaluation of our method on unseen sections from a different macaque yields promising results with a true positive rate of 0.90. The code for our method is available at https://github.com/v-sundaresan/

The size of monkey brains are influenced by social interactions, a new study revealed, finding more friends in a group leads to larger social regions in the brain. A team of researchers from the University of Pennsylvania in Philadelphia, studied the brains, and social interactions of a group of rhesus macaques living on Cayo Santiago, an island off the coast of Puerto Rico. They found that the number of social connections predicted the size of key nodes in parts of the brain responsible for social decision-making and empathy. Though all these findings relate specifically to free-ranging rhesus macaques, they have possible implications for human behavior, in particular to understanding neuro-developmental disorders like autism, according to the team. Researchers determined that, for macaques with more grooming partners, the mid–superior temporal sulcus (STS) and ventral-dysgranular insula grew larger.