Probing the processing of facial expressions in monkeys via time perception and eye tracking

Medium Spiny Neurons Mediate Timing Perception in Coordination with Prefrontal Neurons in Primates

Timing perception is a fundamental cognitive function that allows organisms to navigate their environment effectively, encompassing both prospective and retrospective timing. Despite significant advancements in understanding how the brain processes temporal information, the neural mechanisms underlying these two forms of timing remain largely unexplored. In this study, it aims to bridge this knowledge gap by elucidating the functional roles of various neuronal populations in the striatum and prefrontal cortex (PFC) in shaping subjective experiences of time. Utilizing a large-scale electrode array, it recorded responses from over 3000 neurons in the striatum and PFC of macaque monkeys during timing tasks. The analysis classified neurons into distinct groups and revealed that retrospective and prospective timings are governed by separate neural processes. Specifically, this study demonstrates that medium spiny neurons (MSNs) in the striatum play a crucial role in facilitating these timing processes. Through cell-type-specific manipulation, it identified D2-MSNs as the primary contributors to both forms of timing. Additionally, the findings indicate that effective processing of timing requires coordination between the PFC and the striatum. In summary, this study advances the understanding of the neural foundations of timing perception and highlights its behavioral implications.

A deep learning lightweight model for real-time captive macaque facial recognition based on an improved YOLOX model

Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare. However, reliably identifying individual macaques in group environments remains a significant challenge. This study introduces ACE-YOLOX, a lightweight facial recognition model tailored for captive macaques. ACE-YOLOX incorporates Efficient Channel Attention (ECA), Complete Intersection over Union loss (CIoU), and Adaptive Spatial Feature Fusion (ASFF) into the YOLOX framework, enhancing prediction accuracy while reducing computational complexity. These integrated approaches enable effective multiscale feature extraction. Using a dataset comprising 179 400 labeled facial images from 1 196 macaques, ACE-YOLOX surpassed the performance of classical object detection models, demonstrating superior accuracy and real-time processing capabilities. An Android application was also developed to deploy ACE-YOLOX on smartphones, enabling on-device, real-time macaque recognition. Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool, offering an important foundation for future studies in macaque facial expression recognition, cognitive psychology, and social behavior.

Neuronal Mechanisms Underlying Face Recognition in Non-human Primates

Humans and primates rely on visual face recognition for social interactions. Damage to specific brain areas causes prosopagnosia, a condition characterized by the inability to recognize familiar faces, indicating the presence of specialized brain areas for face processing. A breakthrough finding came from a non-human primate (NHP) study conducted in the early 2000s; it was the first to identify multiple face processing areas in the temporal lobe, termed face patches. Subsequent studies have demonstrated the unique role of each face patch in the structural analysis of faces. More recent studies have expanded these findings by exploring the role of face patch networks in social and memory functions and the importance of early face exposure in the development of the system. In this review, we discuss the neuronal mechanisms responsible for analyzing facial features, categorizing faces, and associating faces with memory and social contexts within both the cerebral cortex and subcortical areas. Use of NHPs in neuropsychological and neurophysiological studies can highlight the mechanistic aspects of the neuronal circuit underlying face recognition at both the single-neuron and whole-brain network levels.

From beasts to bytes: Revolutionizing zoological research with artificial intelligence

Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.