Abstract: Diversity, uncertainty and suddenness of unexpected faults bring a challenge for fault tolerant control due to the lack of valid data especially for a fault during an early stage. In this ...

Learning speed depends on both task structure and neural dynamics prior to learning, yet a theory connecting them has been missing. Inspired by the fluctuation-response relation, we derive two ...

The evolution of neural networks is a fascinating story filled with innovation, groundbreaking breakthroughs, near-fatal setbacks, and remarkable comebacks. From early concepts rooted in neuroscience ...

In 1943, a pair of neuroscientists were trying to describe how the human nervous system works when they accidentally laid the foundation for artificial intelligence. In their mathematical framework ...

The world, and countless generations of interactions with it, coaxed our brains to evolve in the unique way that humans perceive reality. And yet, thanks to the past century's developments in ...

Machine learning (ML) has transformed from a theoretical discipline into a fundamental component of modern technology. Over the past several decades, it has evolved from mathematical foundations to ...

Hebb Network: Theory and Algorithm Donald Hebb's eponymous learning rule, coined in 1949, embodies the principle "cells that fire together, wire together." The Hebbian learning algorithm strengthens ...

In your brain, neurons are arranged in networks big and small. With every action, with every thought, the networks change: neurons are included or excluded, and the connections between them strengthen ...

The basal ganglia (BG) contribute to reinforcement learning (RL) and decision-making, but unlike artificial RL agents, it relies on complex circuitry and dynamic dopamine modulation of opponent ...

Replay of neuronal sequences in the hippocampus during resting states and sleep play an important role in learning and memory consolidation. Consistent with these functions, replay sequences have been ...