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 ...

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 ...

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 ...

Neural nets get a whole lot more complicated than this, but this is the essential structure: different places within a network are represented by nodes (circles) and connections between them ...

A typical recurrent neural network called zeroing neural network (ZNN) was developed for time-varying problem-solving in a previous study. Many applications result in time-varying linear equation and ...

A Physics-Informed Neural Network (PINN) framework for solving partial differential equations (PDEs) with FastAPI integration. This project implements PINNs for various physical systems including ...