94 citations · 163 across the 15 of their papers we have counts for
9 papers · 1 filter
On the role of neurogenesis in overcoming catastrophic forgetting
German I. Parisi, Xu Ji, Stefan Wermter
Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated. In this work,…
Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution
Di Fu, Pablo Barros, German I. Parisi +4
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment. However, artificial multisensory perc…
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning
Francisco Cruz, German I. Parisi, Stefan Wermter
Interactive reinforcement learning (IRL) extends traditional reinforcement learning (RL) by allowing an agent to interact with parent-like trainers during a task. In this paper, we…
Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception
German I. Parisi, Jonathan Tong, Pablo Barros +2
Our daily perceptual experience is driven by different neural mechanisms that yield multisensory interaction as the interplay between exogenous stimuli and endogenous expectations.…
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
German I. Parisi, Jun Tani, Cornelius Weber +1
Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability…
Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration
Jonathan Tong, German I. Parisi, Stefan Wermter +1
When the brain receives input from multiple sensory systems, it is faced with the question of whether it is appropriate to process the inputs in combination, as if they originated…
A Neurorobotic Experiment for Crossmodal Conflict Resolution in Complex Environments
German I. Parisi, Pablo Barros, Di Fu +4
Crossmodal conflict resolution is crucial for robot sensorimotor coupling through the interaction with the environment, yielding swift and robust behaviour also in noisy conditions…
Continual Lifelong Learning with Neural Networks: A Review
German I. Parisi, Ronald Kemker, Jose L. Part +2
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning,…
Expectation Learning for Adaptive Crossmodal Stimuli Association
Pablo Barros, German I. Parisi, Di Fu +2
The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power…