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.
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Velocity-Tuned Oscillators for NeuroSLAM and Spatial Navigation
In this paper, we present the implementation of Velocity Controlled (or tuned) Oscillators (VCO) to model spatial coding and navigation in the mammalian hippocampus. Specifically, we demonstrate these spatial cells by representing a spatial firing map of grid, place, and border cells. Since the VCO is the basis for Oscillatory Interference (OI) models based on the Spatial Envelope Synthesis (SES) approach of hippocampal and entorhinal navigation, we use these models in our hardware implementation to construct more complex spatial cells from simple interference between VCOs. We develop the design of a VCO ASIC chip containing up to 128 independently tuned VCOs.
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- Award ID(s):
- 1824198
- PAR ID:
- 10189739
- Date Published:
- Journal Name:
- 2ND IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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