Computational Neuromorphic Architectures for Modeling the Hippocampus Formation applied to Robotic Navigation

The brain is the most powerful machine that exists, capable of efficiently solving complex problems and far surpassing the capabilities of current systems. In recent decades, neuromorphic engineering has been responsible for the study, design and implementation of hardware and software systems that mimic the behavior, structure and functioning of the brain to achieve such superior capabilities. Within computing systems, memory is a critical component that limits the evolution of these systems by becoming a bottleneck in the flow of information. Additionally, despite the significant growth in computing, the robotics field has seen less significant evolution. Within the brain, the hippocampus stands out for its participation in episodic memory; it can learn and store a large amount of information through association from different brain sensory nuclei, while also being able to recall it based on different fragments of itself. Therefore, this work focuses on the study, design and implementation of neuromorphic memory systems bio-inspired by the hippocampus. A variety of models are proposed to explore different functionalities and paradigms observed in the neuromorphic domain (biological plausibility, analog or digital technology and simulation or emulation). These models, which are capable of learning, forgetting and recalling spiking information, have been developed using Spiking Neural Networks and implemented on various special-purpose hardware platforms for such type of networks. Furthermore, these models have been integrated into robotic platforms for learning, mapping and navigating environments and trajectories. These are the first implementations on specialpurpose hardware platforms for Spiking Neural Networks of fully functional memory models bio-inspired by the hippocampus, paving the way for the development of future, more complex neuromorphic systems.