(since 2016) University of Hamburg, Germany
This project is focused closely on the real-world evaluation of a neurocognitive model. Its goals are (a) to use novel neurocomputational techniques to improve existing models of the superior colliculus (SC) and linked cortical areas; (b) to implement a model of these cortico-collicular networks in a physical robot; and (c) to compare both the model’s neural activity and the robot’s physical behaviour to the neural activity and physical behaviour of biological systems.
More information: www.crossmodallearning.org
Publications associated with this project
- Parisi, G.,Barros, P., Fu, D., Liu, X., Wertmer, S. A Neurorobotic Experiment for Crossmodal Conflict Resolution in Complex Environments. Accepted at IROS 2018.
- Barros, P., Parisi, G., Fu, D., Liu, X., Wertmer, S. (2018, July). Expectation Learning and Crossmodal Modulation with a Deep Adversarial Network. In Neural Networks (IJCNN), 2018 International Joint Conference on (pp. 5314-5321). IEEE.
- Parisi, G. I., Barros, P., Kerzel, M., Wu, H., Yang, G., Li, Z., … & Wermter, S. (2017). A computational model of crossmodal processing for conflict resolution. In IEEE International Conference on Development and Learning and on Epigenetic Robotics (EPIROB-ICDL). IEEE (pp. 33-38).
- P Barros, GI Pasisi, D Fu, X Liu, S Wermter. Expectation Learning for Adaptive Crossmodal Stimuli Association. EUCog Meeting, 2017
- Barros, P., & Wermter, S. (2017, May). A self-organizing model for affective memory. In Neural Networks (IJCNN), 2017 International Joint Conference on (pp. 31-38). IEEE.
- Barros, P., Parisi, G. I., Weber, C., & Wermter, S. (2017). Emotion-modulated attention improves expression recognition: A deep learning model. Neurocomputing, 253, 104-114.