Abstract: Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have been shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machine learning ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

The brain is a highly diverse and heterogeneous network, yet the functional role of this neural heterogeneity remains largely unclear. Despite growing interest in neural heterogeneity, a comprehensive ...

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...

Molecular circuits capable of autonomous learning could unlock novel applications in fields such as bioengineering and synthetic biology. To this end, existing chemical implementations of neural ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

The Kentucky Council on Postsecondary Education (CPE), in partnership with the Department for Community-Based Services (DCBS) and a consortium of postsecondary institutions, kicked off the Community ...

Abstract: Recently, deep learning (DL) has gained significant attention for addressing optimization problems in the field of wireless communication. However, existing methods that train models on a ...