This study provides a normative theory for how Bayesian causal inference can be implemented in neural circuits. In both cognitive processes such as causal reasoning and perceptual inference such as cue integration, the nervous systems need to choose different models representing the underlying causal structures when making inferences on external stimuli. In multisensory processing, for example, the nervous system has to choose whether to integrate or segregate inputs from different sensory modalities to infer the sensory stimuli, based on whether the inputs are from the same or different sources. Making this choice is a model selection problem requiring the computation of Bayes factor, the ratio of likelihoods between the integration and the segregation models. In this paper, we consider the causal inference in multisensory processing and propose a novel generative model based on neural population code that takes into account both stimulus feature and stimulus reliability in the inference. In the case of circular variables such as heading direction, our normative theory yields an analytical solution for computing the Bayes factor, with a clear geometric interpretation, which can be implemented by simple additive mechanisms with neural population code. Numerical simulation shows that the tunings of the neurons computing Bayes factor are consistent with the opposite neurons discovered in dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas for visual-vestibular processing. This study illuminates a potential neural mechanism for causal inference in the brain.

The Public Says'No Thanks' The maker of the eCoffee Energyband says it electrically stimulates your nerves to keep you alert. Researchers are skeptical, and critics see it as a way for China's bosses to keep workers productive. Forget coffee, you can now stay alert by strapping on a wristband that lightly zaps you awake. That's what eCoffee Energyband, a Chinese gadget that sells for just over $100, is claiming to do. First released in late 2023, the product is a lightweight wearable with two electrode pads that sit against the inner wrist.

Environment Conservation Ocean Dolphins may be getting an Alzheimer's-like disease due to this neurotoxin The neurotoxins, found in algal blooms, primarily affect the body's nervous system. Breakthroughs, discoveries, and DIY tips sent every weekday. For marine biologists, dolphins are often viewed as sentinel species, or animals that shed light on the health of the ocean . Along with whales, porpoises, and other cetacean species, dolphins are one way that researchers know to sound the alarm about environmental hazards that might affect the ocean as a whole and potentially humans. In this context, researchers have connected neurotoxins from algal blooms to brain changes associated with an Alzheimer's-like disease in dolphins in Florida.

Are you sure that chatbot isn't alive? Listen to more stories on the Noa app. O ne of the persistent questions in our brave new world of generative AI: If a chatbot is conversant like a person, if it reasons and behaves like one, then is it possibly conscious like a person? Geoffrey Hinton, a recent Nobel Prize winner and one of the so-called godfathers of AI, told the journalist Andrew Marr earlier this year that AI has become so advanced and adept at reasoning that "we're now creating beings." Hinton links an AI's ability to "think" and act on behalf of a person to consciousness: The difference between the organic neurons in our head and the synthetic neural networks of a chatbot is effectively meaningless, he said: "They are alien intelligences."

Are governments and Hollywood films secretly pumping people's minds full of messages which push obedience, alcohol addiction, and disseminate'woke' theories? It's long been known that world governments are fascinated by mind control, with groups like the Central Intelligence Agency (CIA) allegedly conducting sinister experiments on the public. In the 1950s and 60s, the CIA's infamous MKUltra program recruited civilians, mental patients, and drug addicts in an effort to reprogram minds. However, some believe social media has given world governments and entertainment giants new tools to control minds. This includes mind control expert Jason Christoff.

There are many remarkable things about octopuses--they're famously intelligent, they have three hearts, their eyeballs work like prisms, they can change color at will, and they can "see" light with their skin. One of the most striking things about these creatures, however, is the fact that each of their eight arms almost seems to have a mind of its own, allowing an octopus to multitask in a manner that humans can only dream about. At the heart of each arm is a structure known as the axial nervous cord (ANC), and a new study published January 15 in Nature Communications examines how the structure of this cord is fundamental to allowing the arms to act as they do. Cassady Olson, first author on the paper, explains to Popular Science that understanding the ANC is crucial to understanding how an octopus's arms work: "You can think of the ANC as equivalent to a spinal cord running down the center of every single arm." Olson explains that "there are many gross similarities [between the ANC and vertebrates' spinal cords]--there is a cell body region, a neuropil region, and long tracts to connect the arms and brains in each."

Abstract--Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect. Optimizing the prenatal ultrasound diagnosis process can significantly reduce Ultrasonography is popular as a non-invasive and radiationfree the workload of the sonographer; therefore, the application of prenatal diagnostic method for its convenience and low artificial intelligence (AI) and deep learning (DL) techniques cost [1]. Antenatal ultrasound is a crucial imaging tool during in ultrasound imaging can significantly speed up the prenatal pregnancy. It not only assesses fetal growth and development examination process while improving the accuracy and consistency and detects congenital anomalies, but also provides important of the diagnosis. Deep learning, a subset of AI, automatically extracts ultrasound, physicians can assess the presence of congenital features from large amounts of data and performs efficient anomalies in the fetus with the help of two-dimensional (2D) pattern recognition and prediction using deep neural network and three-dimensional (3D) imaging, thus helping to significantly models [5].

Developing strong AI could provide a powerful tool for addressing social and scientific challenges. Neural networks (NNs), inspired by biological systems, have the potential to achieve this. However, weight optimization techniques using error backpropagation are not observed in biological systems, raising doubts about current NNs approaches. In this context, Itoh (2024) solved the MNIST classification problem without using objective functions or backpropagation. However, weight updates were not used, so it does not qualify as machine learning AI. In this study, I develop a machine learning method that mimics biological neural systems by implementing Hebbian learning in NNs without backpropagation and optimization method to solve the MNIST classification problem and analyze its output. Development proceeded in three stages. In the first stage, I applied the Hebbian learning rule to the MNIST character recognition algorithm by Itoh (2024), resulting in lower accuracy than non-Hebbian NNs, highlighting the limitations of conventional training procedures for Hebbian learning. In the second stage, I examined the properties of individually trained NNs using norm-based cognition, showing that NNs trained on a specific label respond powerfully to that label. In the third stage, I created an MNIST character recognition program using vector norm magnitude as the criterion, achieving an accuracy of approximately 75%. This demonstrates that the Hebbian learning NNs can recognize handwritten characters without objective functions, backpropagation, optimization processes, and large data set. Based on these results, developing a mechanism based on norm-based cognition as a fundamental unit and then increasing complexity to achieve indirect similarity cognition should help mimic biological neural systems and contribute to realizing strong AI.

In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined four specific diseases. We assessed four LLMs, BioBERT, XLNet, and BERT, as well as a novel base model (Last-BERT). BioBERT, which was pre-trained on medical data, demonstrated superior performance in medical text classification (97% accuracy). Surprisingly, XLNet followed closely (96% accuracy), demonstrating its generalizability across domains even though it was not pre-trained on medical data. LastBERT, a custom model based on the lighter version of BERT, also proved competitive with 87.10% accuracy (just under BERT's 89.33%). Our findings confirm the importance of specialized models such as BioBERT and also support impressions around more general solutions like XLNet and well-tuned transformer architectures with fewer parameters (in this case, LastBERT) in medical domain tasks.

That was the average length of time an adult could focus on a screen for in 2021, according to research by Gloria Mark, a professor of informatics at the University of California. Twenty years ago, in 2004, that number stood at two-and-a-half minutes. Our attention spans – how long we're able to concentrate without being distracted – are shrinking. Our focus – how intensely we can think about things – is suffering too. The causes: technology that's designed to demand our attention; endless tools for procrastination at our fingertips; rising stress and anxiety disorders; and poor sleep quality.