Babak Mazloom‐Nezhad Maybodi

About

Babak Mazloom‐Nezhad Maybodi is a neuromorphic engineer whose research focuses on creating highly efficient, event-based spiking neural networks (SNNs) in hardware. His major contributions center on developing digital multiplier-less architectures that enable SNNs to learn context-dependent tasks through reinforcement learning—a critical step toward mimicking the brain’s remarkable performance-resources trade-off. By eliminating multipliers, his designs achieve superior energy efficiency and compactness, making them ideal for real-time, low-power applications. His most-cited work (2020, 13 citations) demonstrates a fully digital SNN architecture that learns context-dependent behaviors, while subsequent papers (2019, 5 citations; 2020, 3 citations) refine these event-driven, multiplier-less models. Collectively, his research addresses the challenging problem of learning in SNNs, bridging the gap between biological realism and practical hardware implementation. Mazloom‐Nezhad Maybodi’s work is pivotal for advancing neuromorphic computing, offering a path toward brain-inspired systems that are both powerful and resource-efficient.

Research Focus

Key Achievements

Top Papers

1. 1
   Digital Multiplier-Less Spiking Neural Network Architecture of Reinforcement Learning in a Context-Dependent Task

   13 citations · 2020
2. 2
   Digital Multiplier-less Event-Driven Spiking Neural Network Architecture for Learning a Context-Dependent Task

   5 citations · 2019
3. 3
   A Digital Multiplier-less Neuromorphic Model for Learning a Context-Dependent Task

   3 citations · 2020

Key Collaborators

Contact & Links