Daniel Casanueva‐Morato
Papers
4
Total Citations
19
H-Index
2
About
Daniel Casanueva‐Morato is a rising figure in neuromorphic engineering, a field dedicated to mimicking the brain’s extraordinary computational efficiency in silicon. His research focuses on developing bioinspired, spike-based models of the hippocampus and posterior parietal cortex—brain regions critical for spatial navigation and memory. His most cited work (2023, 10 citations) introduces a novel framework for robot navigation and environment “pseudomapping,” demonstrating how spiking neural networks can replicate biological pathfinding. Building on this, he has advanced trajectory learning and recall with an analog sequential hippocampal memory model (2024, 2 citations) and explored its robustness for real-world applications. Notably, his 2024 study (5 citations) integrates a hippocampus memory model into a neuromorphic robotic arm for precise trajectory navigation, while his 2025 work (2 citations) proposes a closed-loop, event-driven control system for smooth, spike-based robotic arm movement. By bridging theoretical neuroscience with tangible hardware implementations, Casanueva‐Morato is pushing the boundaries of low-power, brain-like computing. His contributions are paving the way for autonomous systems that learn and navigate with the elegance of the brain itself.
Research Focus
Key Achievements
Top Papers
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