A biphasic structural plasticity rule interacts with homeostatic synaptic scaling to maintain firing rate homeostasis in neural networks.

The shift from static inference to real-time autonomous agents is driving explosive demand for custom silicon, low-latency ...

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

The study’s authors note that small neural networks — simplified versions of the neural networks typically used in commercial ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

These waves are at least twice as tall as the surrounding waves. They’re unpredictable. They can come from unexpected ...

Neural Texture Compression (NTC) is a new technique that improves texture quality while reducing VRAM usage. It relies on a specialized neural network trained to compress ...

In this paper, a novel approach leveraging artificial neural networks is introduced to approximate solutions for partial differential equations. The one-dimensi ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...

As an extension of the Lyapunov equation, the time-varying plural Lyapunov tensor equation (TV-PLTE) can carry multidimensional data, which can be solved by zeroing neural network (ZNN) models ...