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

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like telling a leaf apart from a rock. But they have struggled to build ...

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 ...

Inspired by microscopic worms, Liquid AI’s founders developed a more adaptive, less energy-hungry kind of neural network. Now the MIT spin-off is revealing several new ultraefficient models.

The structure of KANs is similar to that of conventional neural networks. The weights do not have a fixed numerical value, however. Instead they correspond to a function: w (x).