The representation of individual memories in a recurrent neural network can be efficiently differentiated using chaotic recurrent dynamics.

A new study suggests that the brain processes information with high efficiency by synchronizing the physical wiring of neural ...

A hybrid model combining LM, GA, and BP neural networks improves TCM's diagnostic accuracy for IPF, achieving 81.22% ...

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. Neural networks are the ...

For the past few years, the recipe for building smarter artificial intelligence has been simple: make it bigger. Add more ...

When you try to solve a math problem in your head or remember the things on your grocery list, you’re engaging in a complex neural balancing act — a process that, according to a new study by Brown ...

Compared to other regression techniques, a well-tuned neural network regression system can produce the most accurate prediction model, says Dr. James McCaffrey of Microsoft Research in presenting this ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write ...

Researchers have developed a hybrid CFD-neural network model for predicting TAIs in hydrogen-fueled turbines, improving ...

This valuable study links psychological theories of chunking with a physiological implementation based on short-term synaptic plasticity and synaptic augmentation. The theoretical derivation for ...