Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system

Abstract

It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior. Understanding biological intelligence, however, is a fundamental goal for several distinct disciplines. Translating our understanding of intelligence to machines is a fundamental problem in robotics. Propelled by new advancements in Neuroscience, we developed a spiking neural network (SNN) that draws from mounting experimental evidence that a number of individual neurons is associated with spatial navigation. By following the brain's structure, our model assumes no initial all-to-all connectivity, which could inhibit its translation to a neuromorphic hardware, and learns an uncharted territory by mapping its identified components into a limited number of neural representations, through spike-timing dependent plasticity (STDP). In our ongoing effort to employ a bioinspired SNN-controlled robot to real-world spatial mapping applications, we demonstrate here how an SNN may robustly control an autonomous robot in mapping and exploring an unknown environment, while compensating for its own intrinsic hardware imperfections, such as partial or total loss of visual input.

arXiv comment8 pages, 3 Figures, International Conference on Neuromorphic Systems (ICONS 2018)

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Cited by in corpus (4)

• Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments
• Applications of Spiking Neural Networks in Visual Place Recognition
• A Neural-Astrocytic Network Architecture: Astrocytic calcium waves modulate synchronous neuronal activity
• Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM