marktechpost

On-Chip Implementation of Backpropagation for Spiking Neural Networks on Neuromorphic Hardware

Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. However, implementing the backpropagation algorithm, a ...
GitHub

A novel backpropagation algorithm based on negated kurtosis loss for training shallow, convolutional, and deep neural networks

The conventional backpropagation (BP) algorithm remains the most widely used approach for training neural networks (NNs), including shallow NN (SNN), convolutional NN (CNN), deep NN (DNN), and deep ...
GitHub

Simple Feedforward - Backpropagation Neural Network

The ANN consists of a single hidden layer and is trained to minimize the mean squared error (MSE) between predicted and actual values. This script serves as a ...
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. Vivek Yadav, an engineering manager from ...
Semiconductor Engineering

Backpropagation Algorithm On Neuromorphic Spiking HW (U. Of Zurich, ETH Zurich, LANL)

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...
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 ...