Nikola K. Kasabov

Cognitive Neuroscience · 2000–2017

A researcher in the Science corpus: 10 papers; trusts 47 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 56 citations into it; #2,714 of 10,852 by trust here, #5,528 by citations
• Electrical and Electronic Engineering — 34 citations into it; #3,299 of 16,539 by trust here, #9,902 by citations
• Cellular and Molecular Neuroscience — 24 citations into it
• Model Architectures — 14 citations into it
• Neuroscience — 14 citations into it
• Artificial Intelligence — 12 citations into it; #3,859 of 35,345 by trust here, #12,244 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Radiology, Nuclear Medicine and Imaging — 9 citations into it

Whom Nikola K. Kasabov trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Teuvo Kohonen
2. R. M. French
3. Russell Reed
4. Bernd Fritzke
5. Masumi Ishikawa
6. David E. Goldberg
7. Mark H. Johnson
8. Babak Hassibi
9. Sen Song
10. Eugene M. Izhikevich

Who trusts Nikola K. Kasabov overall

1. Edwin David Lughofer
2. J. J. Rubio
3. Plamen Parvanov Angelov
4. Kai Keng Ang
5. Dejan Dovzan
6. Dongrui Wu
7. Hai-Jun Rong
8. Shuang Feng
9. Chia-Feng Juang
10. Yang Gao

Papers from Nikola K. Kasabov's perspective

Ordered by how strongly the researchers Nikola K. Kasabov trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014) — 435 citations
• Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. (2013) — 383 citations
• Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (2017) — 72 citations
• Evolving spiking neural networks for audiovisual information processing (2010) — 152 citations
• Evolving Intelligent Systems: Methodology and Applications (2010) — 323 citations
• Evolving Connectionist Systems: The Knowledge Engineering Approach (2007) — 331 citations
• Incremental linear discriminant analysis for classification of data streams (2005) — 370 citations
• DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction (2002) — 1,299 citations
• Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning (2001) — 501 citations
• Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra (2000) — 84 citations