Maxim Bazhenov

Cognitive Neuroscience · 2000–2022

A researcher in the Science corpus: 17 papers; trusts 183 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 347 citations into it; #2,404 of 10,852 by trust here, #1,681 by citations
• Cellular and Molecular Neuroscience — 253 citations into it; #352 of 2,998 by trust here, #442 by citations
• Artificial Intelligence — 179 citations into it
• Neuroscience — 111 citations into it
• Computer Vision and Pattern Recognition — 106 citations into it
• Meta & Continual Learning — 82 citations into it
• Electrical and Electronic Engineering — 61 citations into it; #10,559 of 16,539 by trust here, #8,176 by citations
• Experimental and Cognitive Psychology — 47 citations into it

Whom Maxim Bazhenov trusts overall

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

1. Igor Timofeev
2. Geoffrey E. Hinton
3. Anthony V. Robins
4. Robert Stickgold
5. Yoshua Bengio
6. Yann LeCun
7. Kaiming He
8. Alex Krizhevsky
9. Mircea Steriade
10. Gilles Laurent

Who trusts Maxim Bazhenov overall

1. Eugene M. Izhikevich
2. Giri Panamoottil Krishnan
3. N. A. Cayco-Gajic
4. Gerald M. Edelman
5. Kyle S. Honegger
6. Umberto Olcese
7. Robert A. A. Campbell
8. C. Poo
9. Yoshinori Aso
10. Tyler L. Hayes

Papers from Maxim Bazhenov's perspective

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

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

Their papers

• Heterosynaptic Plasticity Prevents Runaway Synaptic Dynamics (2013) — 91 citations
• Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning (2016) — 23 citations
• Biological underpinnings for lifelong learning machines (2022) — 294 citations
• Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation (2022) — 30 citations
• Can sleep protect memories from catastrophic forgetting? (2019) — 64 citations
• Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021) — 152 citations
• Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks (2020) — 28 citations
• Bidirectional Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Spiking Activity During NREM Sleep. (2020) — 31 citations
• Biologically Inspired Sleep Algorithm for Reducing Catastrophic Forgetting in Neural Networks (2020) — 7 citations
• Biologically inspired sleep algorithm for artificial neural networks (2019) — 21 citations
• Differential roles of sleep spindles and sleep slow oscillations in memory consolidation (2017) — 84 citations
• Cellular and neurochemical basis of sleep stages in the thalamocortical network (2016) — 89 citations
• Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations (2016) — 92 citations
• Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit (2015) — 41 citations
• Olfactory representations by Drosophila mushroom body neurons. (2008) — 424 citations
• Thalamocortical oscillations (2006) — 67 citations
• Origin of slow cortical oscillations in deafferented cortical slabs. (2000) — 706 citations