Neural networks have revolutionized the fields of artificial intelligence (AI) and machine learning by providing a flexible, ...

They're simpler and fit static input-output tasks, like image classification. Recurrent Neural Networks (RNNs), on the other hand, loop data within the network, maintaining a "memory" of prior ...

A team of researchers at Western University has now taken a major step toward opening these AI black boxes. By applying ...

This chapter considers a class of neural networks that have a recurrent structure, including Grossberg network, Hopfield network, and cellular neural networks. The Hopfield network is a form of ...

This repository contains a simple implementation of a recurrent neural network. It allows you to train a single-layer RNN with stochastic gradient descent and backpropagation through time (BPTT). This ...

If you’re not, you may want to head over to Implementing A Neural Network From Scratch, which guides you through the ideas and implementation behind non-recurrent networks. This post is inspired by ...

In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks." ...

Abstract: A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision ...

Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...