Mikael Djurfeldt
Jülich Aachen Research Alliance, KTH Royal Institute of Technology
Papers
2
Total Citations
26
H-Index
2
About
Mikael Djurfeldt is a leading researcher in computational neuroscience, specializing in the integration of large-scale neural simulations with robotic and sensory systems. His primary research areas include spiking neural network modeling, multi-scale simulation tools, and sensorimotor adaptation. Djurfeldt’s major contribution lies in developing technical frameworks that enable closed-loop interactions between neural simulators and physical robots. His 2016 paper, "Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS" (24 citations), demonstrates a pioneering method for coupling realistic neural models with robotic platforms, allowing for richer, more ecologically valid stimuli in experiments. This work bridges the gap between theoretical neural dynamics and embodied behavior. Additionally, his 2012 study on prism-adaptation (2 citations) explores how neural systems learn and adapt sensorimotor contingencies, offering insights into plasticity and learning mechanisms. Djurfeldt’s contributions are vital for advancing neurorobotics and understanding how neural computations give rise to adaptive behavior in real-world environments.
Research Focus
Key Achievements
Top Papers
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- 2