Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs through layers, calculating activations, and preparing data for ...

Recently, the iGaN Laboratory led by Professor Haiding Sun at the School of Microelectronics, University of Science and Technology of China (USTC), together with the team of academician Sheng Liu from ...

Abstract: This paper investigates the controllability of a broad class of recurrent neural networks widely used in theoretical neuroscience, including models of large-scale human brain dynamics.

Taking a page from the private insurance industry’s playbook, the Trump administration will launch a program next year to find out how much money an artificial intelligence algorithm could save the ...

In this important study, the authors model reinforcement-learning experiments using a recurrent neural network. The work examines if the detailed credit assignment necessary for back-propagation ...

The overwhelming majority of tech industry workers use artificial intelligence on the job for tasks like writing and modifying code, a new Google study has found. The report, coming from Google’s DORA ...

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...

Abstract: Shifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in ...

This repository contains the official implementation for the paper "Evolving Spatially Embedded Recurrent Spiking Neural Networks for Control Tasks." The code implements a framework for evolving ...