eLife

Asymmetric neural entrainment at resonance frequencies underlies unilateral spatial neglect

This valuable study uses EEG and computational modeling to investigate hemispheric oscillatory asymmetries in unilateral spatial neglect. The work benefits from rare patient data and a careful ...

MicroCloud Hologram Inc. Releases Hybrid Quantum-Classical Convolutional Neural Network, Achieving New Breakthrough in MNIST Multi-Class Classification

MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, proposed a Quantum Convolutional Neural Network (QCNN) based on hybrid quantum-classical learning and ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ...
GitHub

MACHINE LEARNING FOR QUANTUM NOISE REDUCTION USING DENSITY MATRIX

Quantum computation has the potential for exponential speedup of classical systems in some applications, such as cryptography, simulation of molecular behavior, and optimization. Nevertheless, quantum ...
eLife

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

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 ...
IEEE

On the representation of piecewise quadratic functions by neural networks

Abstract: Neural networks (NNs) are commonly used to approximate functions based on data samples, as they are a universal function approximator for a large class of functions. However, choosing a ...
Frontiers

AN AI METHODOLOGY TO REDUCE TRAINING INTENSITY, ERROR RATES. AND SIZE OF NEURAL NETWORKS - Thaddeus J. Kobylarz, Ph.D

The final, formatted version of the article will be published soon. Abstract - Massive computing systems are required to train neural networks. The prodigious amount of consumed energy makes the ...
Scientific Research Publishing

Leshno, M., Lin, V.Y., Pinkus, A. and Schocken, S. (1993) Multilayer Feedforward Networks with a Nonpolynomial Activation Function Can Approximate Any Function. Neural Networks ...

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...
IEEE

Compactly Hardware Implementation of a High-Speed Activation Function Based on Phase-Change Memory in Neural Network

Abstract: Activation function (AF) is a pivotal yet resource-intensive component in artificial neural network (ANN) hardware implementations. Digital AFs offer high accuracy but require data ...
Hosted on MSN

What Is An Activation Function In A Neural Network? (Types Explained Simply)

Confused about activation functions in neural networks? This video breaks down what they are, why they matter, and the most common types — including ReLU, Sigmoid, Tanh, and more! #NeuralNetworks ...