Emulating the Functionality of Rodents’ Neurobiological Navigation and Spatial Cognition Cells in a Mobile Robot Peter J. Zeno Department of Computer Science and Engineering University of Bridgeport Bridgeport, CT

Outline Path Integration (PI) As observed in rodents (mammals), other animals and insects. As performed in the “ratbot” Issues Full PI Model Purpose of Using PI in Autonomous Mobile Robot Realizing Spatial Cognition in a Machine Grid Cells Emulated Functionality of Grid Cells in an FPGA

Path Integration (PI) in Rodents Rodents (mammals), some animals and insects: Proprioceptive Stimuli – Feedback from muscle nerves (judgement of distance covered). Vestibular Stimuli – Direction of travel based on feedback from inner ear. Conceptually, the result is a vector in memory, where: magnitude = distance, and direction = relative heading. Return vector to “home” is somehow calculated at each turn the rodent takes.

Path Integration (cont.) PI portion of navigation

Path Integration (cont.) Example of rodent foraging path (black) with mentally calculated return vectors (red).

Path Integration - Ratbot The ratbot/autonomous mobile robot uses input from motor encoders (relative distance traveled, and a MEMS based gyro for heading and delta heading (angle). From the gathered and stored vector data, the return-to-home vector is calculated at each turn using the law of sines and cosines: r1 = sqrt(d12 + d22 – 2*d1*d2*cos Ø1) θ1 = arcsin((d1/r1)*sin Ø1)

Path Integration – Ratbot (cont.) Ratbot’s PI Sensors Internal Stimuli: Proprioceptive Stimuli – Motor encoders for distance traveled for each leg of journey. Vestibular Stimuli – MEMs based gyro for heading data External Stimuli: Forward facing ultrasonic sensor for object detection.

Path Integration - Issues In calculations made by the ratbot, as well as a rodent learning a new environment, having very poor external stimuli (sight, smell, touch, sound, etc.) causes errors in PI calculations to grow without bound. Therefore, learning a new environment with some degree of accuracy requires the existence and knowledge of some unique landmark(s). Examples include: spaced barriers, direction to sun, unique smells, etc.

Purpose Q: How is PI different than what is currently being used on some mobile robots which have limited visual (or other external stimulus detection) capabilities? A: PI is being used as the basis for a more profound capability development for autonomous robots: the realization and use of spatial cognition in a machine.

Full PI Model Spatial Cognition

Spatial Cognition In mammalian brains, as particularly found in the hippocampus and entorhinal cortex, there are specialized neurons for PI and spatial awareness: Place Cells – *John O’Keefe Border Cells Head Direction Cells Grid Cells – *Edvard Moser & *May-Britt Moser * Awarded 2014 Nobel Prize in Physiology or Medicine

Grid Cells Location in Human Source: www.sciencemag.org

Grid Cells (cont.) Hexagonal firing pattern of a single grid cell: Rat Foraging – Single grid cell firing (red dots) Autocorrelated image of grid cell’s firing over local area.

FPGA Field Programmable Gate Array (FPGA) Programmable implementation of highly parallelizable hardware using EDA tools. Program logic blocks and path/signal connections. Simulate the neural network that feeds a grid cell to make it fire. Add storage of key mapping data to neural network logic cells for full spatial awareness/coverage. Traversed & direction, object/blockage & direction, relative location ID Feed real-time PI data from ratbot’s central microcontroller.

Emulated Functionality of Grid Cells in an FPGA – Spatial Cognition Home

Thank You! Any Questions?