Mihail Prokhorov
Papers
1
Total Citations
2
H-Index
1
About
Mihail Prokhorov is a computational neuroscientist whose research focuses on the dynamics of spiking neural networks, particularly their application to signal processing and parameter estimation. His most-cited work, "Estimation of impulse action parameters using a network of neuronlike oscillators" (2022), introduces a novel method for inferring the characteristics of external periodic impulses by analyzing the spiking activity of coupled, nonidentical FitzHugh–Nagumo oscillators. This contribution bridges nonlinear dynamics and neural computation, offering a biologically inspired approach to detecting and quantifying external stimuli. With 2 citations to date, his work is gaining traction among researchers exploring oscillator-based sensing and neural coding. Prokhorov’s research has implications for developing robust, decentralized systems for real-time parameter estimation in noisy environments, relevant to fields like neuromorphic engineering and biomedical signal analysis. His achievements include advancing the theoretical understanding of how weakly coupled neuronlike oscillators can collectively encode external forces, a step toward more efficient, brain-inspired computational models.
Research Focus
Key Achievements
Top Papers
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