Neural Networks

JournalCitation impact & published research

Neural Networks is a journal indexed in ScholarIQ from OpenAlex. ScholarIQ records 10,339 works, 483,625 citations, an h-index of 248 and an APC (USD) of 3,350.

10,339

Works

483,625

Citations

248

h-index

3,350

APC (USD)

IDs:OpenAlex

What are the most-cited papers on Neural Networks?

ScholarIQmost cited works

ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network

Gail A. Carpenter, Stephen Grossberg, John H. Reynolds

Neural Networks. 1991987 Citations

Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

Moshe Leshno, Valdimir Ya. Lin, Allan Pinkus, Shimon Schocken

Neural Networks. 1993288 CitationsOPEN ACCESS

Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results

Hang Su, Yingbai Hu, Hamid Reza Karimi, Alois Knoll, Giancarlo Ferrigno, Elena De Momi

Neural Networks. 2020206 CitationsOPEN ACCESS

Global stability analysis in delayed Hopfield neural network models

Jiye Zhang, Xuesong Jin

Neural Networks. 2000201 Citations

Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research

Tom Macpherson, Anne K. Churchland, Terry Sejnowski, James J. DiCarlo, Yukiyasu Kamitani, Hidehiko Takahashi, Takatoshi Hikida

Neural Networks. 2021143 CitationsOPEN ACCESS

Related on ScholarIQ

ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network

Paper

Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

Paper

Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results

Paper

Global stability analysis in delayed Hopfield neural network models

Paper

Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research

Paper

Spiking Neural Networks applied to the classification of motor tasks in EEG signals

Paper