Velocity-Controlled Oscillators for Hippocampal Navigation on Spiking Neuromorphic Hardware

Grid, place, and border cells in the mammalian hippocampus and entorhinal cortex perform highly sophisticated navigational tasks with an extremely low power budget. While previous algorithms for simultaneous localization and mapping (SLAM) in robotics have used these cells for inspiration, they have sacrificed the robust, low-power gains achieved with bioplausible models for ease of implementation. This paper presents steps towards robotic navigation with biologically realistic hippocampal models by implementing velocity-controlled oscillators, a basis for any spatially-tuned neuron, on mixed-mode neuromorphic spiking hardware.

I. Introduction

Robotic navigation requires learning new environments, or changes to a known environment, concurrent with real-time localization of the robot within said environment; this methodology, known as Simultaneous Localization And Mapping (SLAM), typically requires mathematically complex computation and the fusion of input data across many sensory modalities. Thus, prototypical SLAM implementations are often real-ized as over-sensored, power hungry devices. This becomes an unsuitable navigation paradigm for low-power mobile robotics applications, where algorithms must be executed at low costs, often in the presence of high noise. Animals, however, are able to navigate new environments and learn salient features with extremely low energy costs and limited sensory information. Accordingly, neurally-inspired approaches to SLAM are quite promising for providing robust, power-starved performance.