To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as ...

How useful a memory is for future situations determines where it resides in the brain, according to a new theory proposed by researchers at HHMI"s Janelia Research Campus and collaborators at UCL. The ...

The results include a comparison between two different basis functions for temporal selectivity and how these generate different predictions for the dynamics of neural populations. The conclusions are ...

Neuroscientists propose a new theory of brain development where cells organize based on lineage rather than long-range signals.

In an increasingly interconnected world, understanding the behavior and structure of complex networks has become essential across disciplines. These ...

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 ...

“In physics—or wherever natural processes seem unpredictable—apparent randomness may be noise or may arise from deeply complex dynamics.” ―James Gleick Source: Geralt/Pixabay Complexity is the ...

The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end ...

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 ...