Shvan Karim

Papers

Total Citations

116

H-Index

About

Shvan Karim is a pioneering researcher at the intersection of neuromorphic computing and fault-tolerant hardware systems. His work focuses on developing biologically inspired neural networks that can self-repair and maintain homeostasis, drawing directly from the resilience mechanisms observed in biological brains. Karim’s major contributions include the introduction of a homeostatic fault tolerance model for spiking neural networks (58 citations), which demonstrated how dynamic plasticity can enable electronic systems to autonomously recover from damage. He further advanced this field with his work on coupled spiking astrocyte neural networks (50 citations), showing how glial cell modulation and retrograde signaling can localize self-repair capabilities. His most recent work (8 citations) implements these principles on FPGA hardware, combining STDP and BCM learning rules to create a practical, fault-tolerant neuromorphic system. By bridging theoretical neuroscience with real-world hardware implementation, Karim’s research offers a compelling path toward more robust, energy-efficient artificial intelligence systems that can operate reliably in unpredictable environments—a critical step for autonomous systems, robotics, and edge computing applications.