Neuromorphic place cells

Neuromorphic place cells

Blog Article

A neuromorphic simultaneous localization and mapping (SLAM) system shows potential for more efficient implementation than its traditional counterpart.At the mean time a neuromorphic model of spatial encoding neurons in silicon could provide insights on the functionality and dynamic Drive Motor W/ Flywheel between each group of cells.Especially when realistic factors including variations and imperfections on the neural movement encoding are presented to challenge the existing hypothetical models for localization.

We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, egocentric place cells, and the typical allocentric place cells.Together, they form a biologically plausible network that could reproduce the localization functionality of place cells observed in rodents.The system consists of a theta chip with 128 theta cell units and an FPGA implementing 4 networks for egocentric place cells formation that provides the capability for tracking on a 11 by 11 place cell grid.

Experimental results validate the robustness of our model when suffering from as much Multi-Axis Bearing Link as 18% deviation, induced by parameter variations in analog circuits, from the mathematical model of theta cells.We provide a model for implementing dynamic neuromorphic SLAM systems for dynamic-scale mapping of cluttered environments, even when subject to significant errors in sensory measurements and real-time analog computation.We also suggest a robust approach for the network topology of spatial cells that can mitigate neural non-uniformity and provides a hypothesis for the function of grid cells and the existence of egocentric place cells.