Working memory (WM), a fundamental cognitive process facilitating the temporary storage, integration, manipulation, and retrieval of information, plays a vital role in reasoning and decision-making tasks. Robust benchmark datasets that capture the multifaceted nature of WM are crucial for the effective development and evaluation of AI WM models. Here, we introduce a comprehensive Working Memory (WorM) benchmark dataset for this purpose. WorM comprises 10 tasks and a total of 1 million trials, assessing 4 functionalities, 3 domains, and 11 behavioral and neural characteristics of WM. We jointly trained and tested state-of-the-art recurrent neural networks and transformers on all these tasks. We also include human behavioral benchmarks as an upper bound for comparison. Our results suggest that AI models replicate some characteristics of WM in the brain, most notably primacy and recency effects, and neural clusters and correlates specialized for different domains and functionalities of WM. In the experiments, we also reveal some limitations in existing models to approximate human behavior. This dataset serves as a valuable resource for communities in cognitive psychology, neuroscience, and AI, offering a standardized framework to compare and enhance WM models, investigate WM's neural underpinnings, and develop WM models with human-like capabilities.

Biologists investigate the case of the lost'bone devourers' that feed on whale carcasses. Osedax is considered an ecosystem engineer. Breakthroughs, discoveries, and DIY tips sent every weekday. If you're leading a group of zombie apocalypse survivors, you don't want to lose sight of the horde of brain-hungry creatures trying to eat you. The same can be said if the zombie worm goes missing from the ocean floor.

Twelve laboratories around the world have joined forces to map neuronal activity in a mouse's brain as it makes decisions. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Neuroscientists from around the world have worked in parallel to map, for the first time, the entire brain activity of mice while they were making decisions. This achievement involved using electrodes inserted inside the brain to simultaneously record the activity of more than half a million neurons distributed across 95 percent of the rodents' brain volume.

Researchers are working on manipulating the digestive systems of wax worms to create a scalable way of disposing of plastic. Plastics that support modern life are inexpensive, strong, and versatile, but are difficult to dispose of and have a serious impact when released into the environment. Polyethylene, in particular, is the most widely produced plastic in the world, with more than 100 million tons distributed annually. Since it can take decades to decompose--and along the way can harm wildlife and degrade into harmful microplastics --its disposal is an urgent issue for mankind. In 2017, European researchers discovered a potential solution.

Breakthroughs, discoveries, and DIY tips sent every weekday. Biologists estimate that four out of five animals on Earth are nematodes (AKA roundworms).The tiny, wriggling, transparent invertebrates are the most abundant creatures on the planet and are found nearly everywhere–from permafrost to the deep ocean. More than one million species make up this ubiquitous group, which includes parasites, decomposers, predators, and more. "They're not about to take over the world, because they already did," says Serena Ding, a biologist at the Max Planck Institute of Animal Behavior in Konstanz, Germany tells Popular Science. "Global worming has already happened."

In George Lucas's classic 1980 film'The Empire Strikes Back', hero Han Solo (Harrison Ford) is frozen in carbonite by the evil Darth Vader. The fictional metal hardened around the heroic space smuggler as it cooled – sealing him in a state of'perfect hibernation'. Carbonite is of course a fictional material, consigned to the realms of the Star Wars galaxy far, far away. But according to one scientist, this scene is not completely the stuff of science-fiction. Dr Alex Baker, a chemist at the University of Warwick, thinks humans could potentially be frozen like Solo with a real-life equivalent.

Detecting slender, overlapping structures remains a challenge in computational microscopy. While recent coordinate-based approaches improve detection, they often produce less accurate splines than pixel-based methods. We introduce a training-free differentiable rendering approach to spline refinement, achieving both high reliability and sub-pixel accuracy. Our method improves spline quality, enhances robustness to distribution shifts, and shrinks the gap between synthetic and real-world data. Being fully unsupervised, the method is a drop-in replacement for the popular active contour model for spline refinement. Evaluated on C. elegans nematodes, a popular model organism for drug discovery and biomedical research, we demonstrate that our approach combines the strengths of both coordinate- and pixel-based methods.

Working memory (WM), a fundamental cognitive process facilitating the temporary storage, integration, manipulation, and retrieval of information, plays a vital role in reasoning and decision-making tasks. Robust benchmark datasets that capture the multifaceted nature of WM are crucial for the effective development and evaluation of AI WM models. Here, we introduce a comprehensive Working Memory (WorM) benchmark dataset for this purpose. WorM comprises 10 tasks and a total of 1 million trials, assessing 4 functionalities, 3 domains, and 11 behavioral and neural characteristics of WM. We jointly trained and tested state-of-the-art recurrent neural networks and transformers on all these tasks.

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."

In this brief and speculative commentary, we explore ideas inspired by neural networks in machine learning, proposing that a simple neural XOR motif, involving both excitatory and inhibitory connections, may provide the basis for a relevant mode of plasticity in neural circuits of living organisms, with homeostasis as the sole guiding principle. This XOR motif simply signals the discrepancy between incoming signals and reference signals, thereby providing a basis for a loss function in learning neural circuits, and at the same time regulating homeostasis by halting the propagation of these incoming signals. The core motif uses a 4:1 ratio of excitatory to inhibitory neurons, and supports broader neural patterns such as the well-known 'winner takes all' (WTA) mechanism. We examined the prevalence of the XOR motif in the published connectomes of various organisms with increasing complexity, and found that it ranges from tens (in C. elegans) to millions (in several Drosophila neuropils) and more than tens of millions (in mouse V1 visual cortex). If validated, our hypothesis identifies two of the three key components in analogy to machine learning models: the architecture and the loss function. And we propose that a relevant type of biological neural plasticity is simply driven by a basic control or regulatory system, which has persisted and adapted despite the increasing complexity of organisms throughout evolution.