Recurrent Neural Networks (RNN): A special type of neural network, RNN is a complex network that uses the output of a node ...

Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...

Artificially engineered biological processes, such as perception systems, remain an elusive target for organic electronics ...

Treating the neurons and me — my mental states — in a synergistic way proved to speed up and enhance healing. It does for ...

A new collaboration between Northwestern University and Georgia Tech has led to the development of an artificial electrochemical neuron that mimics human neural responses, paving the way for smarter ...

Artificial Neural Networks (ANNs) are commonly used for ... training data set to configure the weights associated with each ‘neuron’. Due to the complexity of these ANNs for non-trivial ...

Researchers developed a novel method combining hydrogel-coated microfluidic devices with high-density microelectrode arrays to study neuronal networks. This innovation allows precise spatial and ...

Just as GPUs once eclipsed CPUs for AI workloads, Neural Processing Units (NPUs) are set to challenge GPUs by delivering even ...

Titans architecture complements attention layers with neural memory modules that select bits of information worth saving in the long term.

This transformation is often nonlinear because a neuron might suddenly ... they can be used to emulate neural activity in artificial neural networks. Another useful feature of microring resonators ...

More information: Christian Klos et al, Smooth Exact Gradient Descent Learning in Spiking Neural Networks, Physical Review Letters (2025). DOI: 10.1103/PhysRevLett.134.027301 . On arXiv : DOI: 10. ...