Categories: AI/ML News

Brain-inspired computing: Using noise to regulate information flow in neural networks

Researchers have developed a learning mechanism that uses the natural variability of neural activity—often dismissed as random “noise”—to understand how synapses buried deep inside brain networks—or brain-inspired devices—can adapt to improve the learning capabilities of the networks as a whole.

Brain-inspired learning algorithm realizes metaplasticity in artificial and spiking neural networks

Catastrophic forgetting, an innate issue with backpropagation learning algorithms, is a challenging problem in artificial and spiking neural network (ANN and SNN) research.

September 2, 2023

In "AI/ML News"

o1’s Thoughts on LNMs and LMMs

December 14, 2024

In "FAANG"

Solving brain dynamics gives rise to flexible machine-learning models

Last year, MIT researchers announced that they had built "liquid" neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job and can adapt to changing conditions, for real-world safety-critical tasks, like driving and flying. The flexibility of these…

November 16, 2022

In "AI/ML News"

AI Generated Robotic Content