Saeed Haghiri

Razi University, Kermanshah University of Technology

Papers

Total Citations

109

H-Index

About

Saeed Haghiri is a leading researcher in neuromorphic engineering, specializing in the digital realization of biological neural networks. His work focuses on developing efficient, multiplierless implementations of neuron models—a critical step toward building bio-mimetic processors that can emulate brain function with minimal hardware cost. Haghiri’s most cited paper, “Digital Multiplierless Realization of Two-Coupled Biological Hindmarsh–Rose Neuron Model” (2015, 64 citations), pioneered a method for modeling coupled neurons without complex multipliers, enabling faster, more power-efficient hardware. He extended this approach in “Digital Multiplierless Realization of Coupled Wilson Neuron Model” (2018, 45 citations), demonstrating its versatility across different neuron dynamics. By eliminating multipliers, Haghiri’s designs achieve switching speeds of about 1 ms, making them suitable for real-time applications in robotics and brain-disease research. His contributions bridge computational neuroscience and hardware implementation, offering scalable solutions for studying spiking neural networks. With over 100 combined citations, Haghiri’s work is foundational for engineers and neuroscientists seeking to build low-power, high-speed neuromorphic systems that advance both artificial intelligence and our understanding of the brain.