Papers
3
Total Citations
162
H-Index
3
About
Igor Peric is a leading researcher at the intersection of computational neuroscience and neurorobotics, dedicated to bridging the gap between biologically realistic brain models and physical robotic systems. His primary research areas include spiking neural networks (SNNs), brain-inspired motor control, and embodied cognition. Peric’s most impactful contribution is his work on the Neurorobotics Platform (117 citations), a comprehensive simulation framework that allows researchers to connect artificial brain models to virtual robots, enabling the validation of neural theories in rich, dynamic environments. He has also pioneered methods for robotic grasping using SNNs for anthropomorphic hands and developed a dopamine-modulated spike-timing-dependent plasticity (STDP) learning rule that enables robotic arms to learn target-reaching motions through trial and error, mimicking human motor babbling. This work, which bridges reinforcement learning and synaptic plasticity, has been cited 13 times and represents a significant step toward autonomous, brain-like motor control. Peric’s research is notable for its direct application of neuroscientific principles—such as dopaminergic reward signals—to solve real-world robotics challenges, making him a key figure in the emerging field of neurorobotics.
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