Recent research has employed chemical reaction networks (CRNs), which harness biochemical processes for computations that translate interactions involving biochemical species into graphical form.

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

With no crew, no camera, little money and a lot of words, anyone can become a filmmaker today. Satyen K. Bordoloi chronicles the history of the AI filmmaking movement with an emphasis on the week that ...

Energy and memory: A new neural network paradigm A dynamic energy landscape is at the heart of theorists' new model of memory retrieval Date: May 14, 2025 Source: University of California - Santa ...

A recurrent neural network-based framework to non-linearly model behaviorally relevant neural dynamics by Ingrid Fadelli, Phys.org ...

A neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works.

Furthermore, multiple SC-ML layers can be stacked and wired using additional feedforward weights to construct even larger recurrent networks. Secondly, we demonstrate a method called Hybrid ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.