Nature

Deep physical neural networks trained with backpropagation

However, implementing trained mathematical transformations by designing hardware for strict, operation-by-operation mathematical isomorphism is not the only way to perform efficient machine learning.
Nature

Backpropagation-free spiking neural networks with the forward–forward algorithm

Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantages, training SNNs ...
Quanta Magazine

Neuron Bursts Can Mimic Famous AI Learning Strategy

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI. Every time a human or machine learns how ...
Semiconductor Engineering

In Situ Backpropagation Strategy That Progressively Updates Neural Network Layers Directly ...

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...
InfoQ

Deep Learning Pioneer Geoffrey Hinton Publishes New Deep Learning Algorithm

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Birgitta Böckeler, Distinguished Engineer at ...
Quanta Magazine

Artificial Neural Nets Finally Yield Clues to How Brains Learn

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could. In 2007, some of the leading ...