| ||Real-time Mapping on a Neuromorphic Processor|
Guangzhi Tang and Konstantinos Michmizos
Navigation is so crucial for our survival that the brain hosts a dedicated network of neurons to map our surroundings. Place cells, grid cells, border cells, head direction cells and other specialized neurons in the hip-
pocampus and the cortex work together in planning and learning maps of the environment . When faced with similar navigation challenges, robots have an equally important need for generating a stable and accurate map. In our ongoing effort to translate the biological network for spatial navigation into a spiking neural network (SNN) that controls mobile robots in real-time, we first focused on simultaneous localization and mapping (SLAM), being one of the critical problems in robotics that relies highly on the accuracy of map representation . Our approach allows us to
leverage the asynchronous computing paradigm commonly found across brain areas and therefore has already demonstrated to be a significant energy-efficient solution for 1D SLAM , that can spur the emergence of the new neuromorphic processors, such as Intel’s Loihi  and IBM’s TrueNorth .
In this paper, we expand our previous work by proposing a SNN that forms a cognitive map of an unknown environment and is seamlessly integrated to Loihi.
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|Guangzhi Tang et al.|