--Data-driven Artificial Intelligence (AI) approaches have exhibited remarkable prowess across various cognitive tasks using extensive training data. However, the reliance on large datasets and neural networks presents challenges such as high-power consumption and limited adaptability, particularly in SWaP-constrained applications like planetary exploration. T o address these issues, we propose enhancing the autonomous capabilities of intelligent robots by emulating the associative learning observed in animals. Associative learning enables animals to adapt to their environment by memorizing concurrent events. By replicating this mechanism, neuromorphic robots can navigate dynamic environments autonomously, learning from interactions to optimize performance. This paper explores the emulation of associative learning in rodents using neuromorphic robots within open-field maze environments, leveraging insights from spatial cells such as place and grid cells. By integrating these models, we aim to enable online associative learning for spatial tasks in real-time scenarios, bridging the gap between biological spatial cognition and robotics for advancements in autonomous systems.

"true" quantity we want to estimate

How should the response profiles of these more "heterogeneous" cells be described, and how do they contribute to behavior? In this work, we took a computational approach to addressing these questions.

If we want to use AI for self-driving cars, then surely a good use for it would be to save lives. AI can be used in diagnosis, with many benefits, such as: we don't have to spend time and resources training medical professionals, and we reduce human error in diagnosis. We can also get diagnosis' earlier than we would with a human doctor, early diagnosis is projected to have saved 10 thousand lives! In 2017, there were 1.566 physicians for 1000 people globally, with Germany being the highest with 4.3 physicians per 1000 people. This means that there is an insane amount of pressure on the healthcare system.

The search for novel therapies has long been a trial-and-error process that costs drug companies a vast amount of time and money. Now, with artificial intelligence (AI) set to transform the pharmaceutical industry more than any other emerging technology, a growing number of pharma and biotech groups are harnessing the cutting-edge tech to minimise the hit-and-miss nature of R&D and discover new therapies with previously impossible speed and accuracy. Pharmaceutical Technology delves into the new generation of drug discovery companies leveraging AI to uncover novel treatments. Founded in 2018 by life sciences venture capital company Flagship Pioneering, Massachusetts-based Generate Biomedicines uses machine learning to accelerate the discovery of protein therapeutics. The company's AI-powered Generative Biology platform analyses hundreds of millions of known protein structures, and uses the learned patterns to create novel protein sequences that form the basis of new therapies.

Researchers at the Allen Institute for Cell Science, a Seattle research group founded by Microsoft co-founder Paul Allen, have created the first predictive 3D model of a live human cell. Using the model, scientists can digitally visualize and even manipulate cell behavior on a computer screen. Called the Allen Integrated Cell, the model is the result of deep learning training with tens of thousands of high-quality cell images. It's able to identify subcellular structures and project a 3D, multilayered image of a cell that shows how all its components interact simultaneously -- something that has never been visualized in this way before. "To me, it's the closest thing I've ever seen in science to magic," said Rick Horwitz, Executive Director of the Allen Institute for Cell Science.

A new application of artificial intelligence could help researchers solve medical mysteries ranging from cancer to Alzheimer's. It's a 3D model of a living human cell that lets scientists study the interior structures of a cell even when they can only see the exterior and the nucleus -- the largest structure in a cell. The model was unveiled to the public Wednesday by the Allen Institute for Cell Science in Seattle. The technology is freely available, and Roger Brent, an investigator at the Fred Hutchinson Cancer Research Center in Seattle who was not involved in the tool's development, has been using it for several months. "This lets you see things with a simple microscope that are going to be helpful to researchers all over the world -- including in less affluent places," Brent says.

Researchers at the Allen Institute for Cell Science in Seattle used artificial intelligence to study and learn from images of tens of thousands of cells. Researchers at the Allen Institute for Cell Science in Seattle used artificial intelligence to study and learn from images of tens of thousands of cells. A new application of artificial intelligence could help researchers solve medical mysteries ranging from cancer to Alzheimer's. It's a 3D model of a living human cell that lets scientists study the interior structures of a cell even when they can only see the exterior and the nucleus -- the largest structure in a cell. The model was unveiled to the public Wednesday by the Allen Institute for Cell Science in Seattle.