Mokhtar Nibouche
University of the West of England, Bristol Robotics Laboratory
Papers
9
Total Citations
219
H-Index
6
About
Mokhtar Nibouche is a leading researcher in neuromorphic engineering and real-time bio-inspired signal processing, with a focus on bridging biological neural systems and hardware implementation. His most influential work, "Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach" (128 citations), introduced a pioneering fixed-point arithmetic architecture for modeling large-scale leaky-integrate-and-fire (LIF) neuron networks on FPGAs, enabling biologically plausible spiking neural networks to operate in real-time for control and signal-processing tasks. This foundational contribution, along with earlier designs like the embedded real-time spiking neural network processor (28 citations), established him as a key innovator in FPGA-based neural computing. Nibouche has also made significant strides in biomimetic sensing, developing an artificial whisker sensory system (18 citations) that models rodent facial vibrissae for tactile and haptic applications, and a neuromorphic tactile sensory system for object recognition and texture discrimination. His more recent work explores adaptive cerebellar models for robot audio localization, demonstrating a self-calibrating approach to acoustic environment mapping. With over 200 cumulative citations, Nibouche’s research continues to advance the frontier of real-time, hardware-accelerated neural systems for robotics and control.
Research Focus
Key Achievements
Top Papers
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- 3A Real-Time, FPGA Based, Biologically Plausible Neural Network Processor20 citations · 2005
- 4A Biomimetic Haptic Sensor18 citations · 2005
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- 7Audio Localization for Robots Using Parallel Cerebellar Models4 citations · 2018
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