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

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