Mohamed Benmohammed
Papers
2
Total Citations
13
H-Index
2
About
Dr. Mohamed Benmohammed is a pioneering researcher at the intersection of computational neuroscience and autonomous robotics, specializing in bio-inspired path planning and spiking neural networks (SNNs). His major contributions center on translating hippocampal dynamics—specifically the role of place cells and traveling waves—into robust navigation algorithms for mobile robots. In his highly cited 2017 work, “Robust path planning by propagating rhythmic spiking activity in a hippocampal network model” (7 citations), he demonstrated how rhythmic neural activity can generate efficient, obstacle-avoiding paths. His foundational 2015 paper, “Spike-Time Dependent Plasticity in a Spiking Neural Network for Robot Path Planning” (6 citations), was among the first to integrate STDP learning rules with O’Keefe place cell representations, enabling robots to build cognitive maps of their environment. By bridging the gap between neural coding and real-world navigation, Benmohammed’s work offers a compelling alternative to traditional path planning methods, emphasizing biological realism and energy efficiency. His research is particularly influential for students and engineers exploring neuromorphic computing, autonomous systems, and the application of brain-inspired algorithms to robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2