S.N. Danilin
Papers
1
Total Citations
3
H-Index
1
About
S.N. Danilin is a leading researcher in the field of neuromorphic computing and memristive systems, with a core focus on the design and simulation of artificial neural networks built on metal-oxide memristive devices. His most cited work, "Design and Simulation of Memristor-Based Artificial Neural Network for Bidirectional Adaptive Neural Interface" (2020), introduces a comprehensive methodology for constructing multilayer perceptron (MLP) networks using cross-bar arrays. This approach integrates artificial neural network modeling (ANNM) theory with tolerance theory and experimental design, enabling robust and bidirectional adaptive neural interfaces. With 3 citations, this foundational paper has influenced subsequent studies in hardware-based neural networks and adaptive systems. Danilin’s contributions bridge the gap between theoretical neural network design and practical memristor-based implementations, offering a simulation framework that accounts for device variability and system reliability. His work is particularly notable for advancing the development of energy-efficient, brain-inspired computing architectures, making him a key figure in the evolution of next-generation adaptive neural interfaces and neuromorphic hardware.
Research Focus
Key Achievements
Top Papers
- 1