The ability to analyze the brain's neural connectivity is emerging as a key foundation for brain-computer interface (BCI) ...

New research published in Imaging Neuroscience suggests that general intelligence is supported by the brain’s ability to ...

As you begin your hybrid quantum approach, here are the advantages, use cases and limitations to keep in mind.

Information Theory Meets Deep Neural Networks: Theory and Applications. The previous volume can be viewed here: Volume I Deep Neural Networks (DNNs) have become one of the most popular research ...

A new study suggests that the communication patterns within the brains of individuals with Alzheimer’s disease are less ...

This valuable study uses mathematical modeling and analysis to address the question of how neural circuits generate distinct low-dimensional, sequential neural dynamics that can change on fast, ...

A PCB business card is a great way for electrical engineers to impress employers with their design skills, but the software they run can be just as impressive as the card itself. As a programmer ...

Transformers are a neural network (NN) architecture, or model, that excels at processing sequential data by weighing the ...

The Navier–Stokes partial differential equation was developed in the early 19th century by Claude-Louis Navier and George ...

The demo runs entirely in your browser (no backend required) and shows an animated XOR training visualization.

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 ...