Programs
- M. Tech. in Automotive Engineering -Postgraduate
- Master of Physician Associate (M.PA) – (Medicine, Surgery) 2 Year -Postgraduate
Publication Type : Conference Paper
Publisher : IEEE
Source : 2025 IEEE 5th International Conference on VLSI Systems, Architecture, Technology and Applications (VLSI SATA)
Url : https://doi.org/10.1109/vlsisata65374.2025.11069983
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2025
Abstract : Realizing neuronal networks at the circuit level requires understanding how the brain processes information and modeling neural circuits. This necessitates the realization of the Hodgkin-Huxley (HH) model to replicate the subtle and nonlinear dynamics of realistic biological neurons. This paper aims to implement and compare the dynamics of a traditional HH neuron model modelled using nonlinear differential equations with a highly nonlinear Memristive HH (MHH) model. Verilog HDL was employed to implement both models, and functional verification was conducted using Modelsim. The experimental results suggest that MHH exhibited high sensitivity to external disturbances, as evidenced by the increased rate of action notential discharge. © 2025 IEEE.
Cite this Research Publication : Gattu Hemanth Kumar, Sunitha R, Ramesh Chinthala, Implementation of Memristive Hodgkin-Huxley Neuron Model in Verilog HDL, 2025 IEEE 5th International Conference on VLSI Systems, Architecture, Technology and Applications (VLSI SATA), IEEE, 2025, https://doi.org/10.1109/vlsisata65374.2025.11069983