This valuable computational study presents a conceptually simple and biologically plausible reinforcement-learning framework for motor learning based on policy-gradient methods. The evidence ...

Artificial neural networks, widely recognized for their role in machine learning, are also transforming the study of ordinary differential equations (ODEs), bridging data-driven modeling with ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. This study investigates fault diagnosis, encompassing fault detection, isolation, and ...

Which learning goals must individual computational elements pursue to contribute to a network-level task solution? This local understanding is missing in both biological, but also artificial neural ...

Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China School of Information and Control ...

In 1957, Rosenblatt published pioneering work on the first machine learning algorithm for artificial neurons, known as the perceptron. He helped revolutionize the field of artificial intelligence ...

A variety of advanced machine learning and deep learning algorithms achieve state-of-the-art performance on various temporal processing tasks. However, these methods are heavily energy ...

A critical challenge across biological disciplines is understanding how mutations in genetic sequence change downstream biological function. Measurements linking genotype to phenotype are ...

HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma Adversarial images represent a ...