An Improved Path Integration Mechanism Using Neural Fields Which Implement A Biologically Plausible Analogue To A Kalman Filter

Abstract

Interaction with the world is necessary for both animals and robots to complete tasks. This interaction requires a sense of self, or the orientation of the robot or animal with respect to the world. Creating and maintaining this model is a task which is easily maintained by animals, however can be difficult for robots due to the uncertainties in the world, sensing, and movement of the robot. This estimation difficulty is increased in sensory deprived environments, where no external, inputs are available to correct the estimate. Therefore, self generated cues of movement are needed, such as vestibular input in an animal, or accelerometer input in a robot. In spite of the difficulties, animals can easily maintain this model. This leads to the question of whether we can learn from nature by examining the biological mechanisms for pose estimation in animals. Previous work has shown that neural fields coupled with a mechanism for updating the estimate can be used to maintain a pose estimate through a sustained area of activity called a packet. Analysis of this mechanism however has shown conditions where the field can provide unexpected results or break down due to high accelerations input into the field. This analysis illustrates the challenges of controlling the activity packet size under strong inputs, and a limited speed capability using the existing mechanism. As a result of this, a novel weight combination method is proposed to provide a higher speed and increased robustness. The results of this is an increase of over two times the existing speed capability, and a resistance of the field to break down under strong rotational inputs. This updated neural field model provides a method for maintaining a stable pose estimate. To show this, a novel comparison between the proposed neural field model and the Kalman filter is considered, resulting in comparable performance in pose prediction. This work shows that an updated neural field model provides a biologically plausible pose prediction model using Bayesian inference, providing a biological analogue to a Kalman filter.