Maksim Belyaev
Papers
1
Total Citations
6
H-Index
1
About
Maksim Belyaev is a researcher at the intersection of computational neuroscience and machine learning, with a primary focus on bio-inspired neural network models and nonlinear dynamics. His most notable contribution is the development of a bio-inspired chaos sensor model based on the perceptron neural network, designed to estimate the entropy of spike trains in neurodynamic systems. This work, published in 2023, demonstrates a novel application of machine learning to computational neuroscience by training a perceptron with 50 hidden-layer neurons to approximate fuzzy entropy, offering a powerful tool for analyzing complex neural signals. While his citation count is still growing—with his key paper accumulating 6 citations to date—Belyaev’s work represents an innovative step toward bridging artificial neural networks and biological chaos detection. His research holds promise for advancing our understanding of neural information processing and could have future implications for brain-computer interfaces and neurological diagnostics.
Research Focus
Key Achievements
Top Papers
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