Neural Computation

3.2k papers and 256.5k indexed citations
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About

The 3.2k papers published in Neural Computation in the last decades have received a total of 256.5k indexed citations. Papers published in Neural Computation usually cover Cognitive Neuroscience (1.7k papers), Artificial Intelligence (1.5k papers) and Cellular and Molecular Neuroscience (637 papers) specifically the topics of Neural dynamics and brain function (1.5k papers), Neural Networks and Applications (1.1k papers) and Advanced Memory and Neural Computing (528 papers). The most active scholars publishing in Neural Computation are Jürgen Schmidhuber, Sepp Hochreiter, David Mackay, Terrence J. Sejnowski, Geoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh, Anthony J. Bell, Bernhard Schölkopf and Geoffrey E. Hinton.

In The Last Decade

Neural Computation

3.1k papers receiving 239.3k citations

Fields of papers published in Neural Computation

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Neural Computation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Neural Computation.

Countries where authors publish in Neural Computation

Since Specialization
Citations

This map shows the geographic impact of research published in Neural Computation. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Neural Computation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neural Computation more than expected).

Long Short-Term Memory 1989 2026 2001 2013 43.9k
  1. Long Short-Term Memory (1997)
  2. A Fast Learning Algorithm for Deep Belief Nets (2006)
  3. Backpropagation Applied to Handwritten Zip Code Recognition (1989)
  4. An Information-Maximization Approach to Blind Separation and Blind Deconvolution (1995)
  5. Nonlinear Component Analysis as a Kernel Eigenvalue Problem (1998)
  6. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation (2003)
  7. Learning to Forget: Continual Prediction with LSTM (2000)
  8. Bayesian Interpolation (1992)
  9. Estimating the Support of a High-Dimensional Distribution (2001)
  10. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures (2019)
  11. Universal Approximation Using Radial-Basis-Function Networks (1991)
  12. Fast Learning in Networks of Locally-Tuned Processing Units (1989)
  13. Training Products of Experts by Minimizing Contrastive Divergence (2002)
  14. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms (1998)
  15. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations (2002)
  16. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks (1989)
  17. Adaptive Mixtures of Local Experts (1991)
  18. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017)
  19. The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks (2007)
  20. Neural Networks and the Bias/Variance Dilemma (1992)
  21. The NEURON Simulation Environment (1997)
  22. A Fast Fixed-Point Algorithm for Independent Component Analysis (1997)
  23. Canonical Correlation Analysis: An Overview with Application to Learning Methods (2004)
  24. New Support Vector Algorithms (2000)
  25. A Practical Bayesian Framework for Backpropagation Networks (1992)
  26. Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering (2004)
  27. Improvements to Platt's SMO Algorithm for SVM Classifier Design (2001)
  28. Deep, Big, Simple Neural Nets for Handwritten Digit Recognition (2010)

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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