Bi-directioal Motion Detection: A Neural Intelligent Model For Perception of Cognitive Robots

TL;DR

This paper introduces a novel bi-directional motion detection neural circuit based on spiking neuronal networks, enhancing perception capabilities in cognitive robots with improved symmetry and handling of neuronal blocking issues.

Contribution

It presents a new symmetric neuronal circuit model for bi-directional motion detection in cognitive robots, addressing blocking problems and demonstrating efficient coupling with high-speed networks.

Findings

Effective detection of movement direction in dynamic environments.

Reduced sensory network potential levels in cognitive robots.

High switching rate and efficient coupling with high-speed circuits.

Abstract

In this paper, a new neuronal circuit, based on the spiking neuronal network model, is proposed in order to detect the movement direction of dynamic objects wandering around cognitive robots. Capability of our new approach in bi-directional movement detection is beholden to its symmetric configuration of the proposed circuit. With due attention to magnificence of handling of blocking problems in neuronal networks such as epilepsy, mounting both excitatory and inhibitory stimuli has been taken into account. Investigations upon applied implementation of aforementioned strategy on PIONEER cognitive robot reveals that the strategy leads to alleviation of potential level in the sensory networks. Furthermore, investigation on intrinsic delay of the circuit reveals not only the noticeable switching rate which could be acquired but the high-efficient coupling of the circuit with the other high-speed ones.