Neuromorphic model of hippocampus place cells using an oscillatory interference technique for hardware implementation

Abstract

In this paper, we propose a simplified and robust model for Place cell generation based on the Oscillatory Interference (OI) model concept. Aiming toward hardware implementation in bio-inspired Simultaneous Localization and Mapping (SLAM) systems for mobile robotics, we base our model on logic operations that reduce its computational complexity. The model compensates for parameter variations in the behaviors of the population of constituent Theta cells, and allows the Theta cells to have square-wave oscillation profiles. The robustness of the model, with respect to mismatch in the Theta cell’s base oscillation frequency and gain—as a function of modulatory inputs—is demonstrated. Place cell composed of 48 Theta cells with base frequency variations with a 25% standard deviation from the mean and a gain error with 20% standard deviation from the mean only result in a 20% deformations within the place field and 0.24% outer side lobes, and an overall pattern with 0.0015 mean squared error on average. We also present how the model can be used to achieve the localization and path-tracking functionalities of SLAM. Hence, we propose a model for spatial cell formation using Theta cells with behaviors that are biologically plausible and hardware implementable for real world application in neurally-inspired SLAM.

Neuromorphic place cells

“…It can compensate for the inevitable behavioral variations of either biological neurons or neuromorphic implementations while reducing the interference operation to logic operations. The performance and details of the model are published in [22], though we will introduce its basic structure here for completeness. An important characteristic is that to accommodate variations among theta cells readily, the cell formed by the improved model is downgraded into an egocentric place cell reporting segmented displacement rather than being a location-specific cell in the original OI model.…”

“…To overcome its strict requirements on the oscillators, we previously proposed an improved OI model for neuromorphic implementations that compensates the mismatch between theta cell behaviors with an 'offset reduction' strategy and simplifies the interference operation to logic operations between oscillators with square-wave oscillation profiles. We summarize the principle of the improved model in section 2.2; for a more detailed analysis, please refer to our previous report [22].…”

“…Though the model in [22] provides conciseness and feasibility against realistic parameter and behavioral variations, the spatial cell it forms does not replicate the functionality of a place cell. The model is only capable of indicating one segment of a movement rather than tracking the location of the agent throughout a trail.…”

“…They are egocentric due to their response region, or place field, are centered in a coordinate system that moves along with the animal, while the original place cells are fixed in the world coordinate system. The model in [22] proposed a vector accumulation strategy for localization by resetting the egocentric reference frame according to the firing of egocentric place cells. Here, we realize that concept by proposing a place cell network model that receives input from the egocentric place cells and accumulates the movement in a neuromorphic fashion.…”

Editorial: ‘Bioinspired Adaptive Intelligent Robots’

“…The model can be also applied to an event-based camera's active vision system, enabling it to overcome an orientation misalignment between the left and right cameras without any manual calibration. Zhaoqi et al (2022) propose a simplified and robust model for hippocampus-like place cells generation, based on the oscillatory interference model concept, to implement a bio-inspired simultaneous localization and mapping (SLAM) system for mobile robotics that can be used to achieve the localization and path-tracking functionalities of SLAM.…”