While conventional spiking neural ... networks (SNNs) provide low power consumption, they often lack of sufficient long-term memory capabilities. To address this, we propose the Plastic Recurrent ...

Many conventional computer architectures are ill-equipped to meet the computational demands of machine learning-based models. In recent years, some engineers have thus been trying to design ...

Humans and certain animals appear to have an innate capacity to learn relationships between different objects or events in ...

One approach center on recurrent neural networks, a type of neural circuit model that consists of many recurrently connected units. Recurrent neural networks can be trained to perform decision ...

The integration of neuromorphic computing and deep learning is revolutionizing computational neuroscience, offering new methods for interpreting complex ...

“They optimized their model architecture using a battery of engineering tricks—custom communication schemes between chips, reducing the size of fields to save memory, and innovative use of the ...

Glenn Youngkin (R) barred state employees Tuesday from using artificial intelligence (AI) models developed by Chinese startup DeepSeek on state-issued devices and state-run networks.