A spiking neural network model for obstacle avoidance in simulated prosthetic vision
Journal article, 2017

Limited by visual percepts elicited by existing visual prosthesis, it’s necessary to enhance its functionality to fulfill some challenging tasks for the blind such as obstacle avoidance. This paper argues that spiking neural networks (SNN) are effective techniques for object recognition and introduces for the first time a SNN model for obstacle recognition to as- sist blind people wearing prosthetic vision devices by modelling and classifying spatio- temporal (ST) video data. The proposed methodology is based on a novel spiking neural network architecture, called NeuCube as a general framework for video data modelling in simulated prosthetic vision. As an integrated environment including spiking trains en- coding, input variable mapping, unsupervised reservoir training and supervised classifier training, the NeuCube consists of a spiking neural network reservoir (SNNr) and a dy- namic evolving spiking neural network classifier (deSNN). First, input data is captured by visual prosthesis, then ST feature extraction is utilized in the low-resolution prosthetic vi- sion generated by prostheses. Finally such ST features are fed to the NeuCube to output classification result of obstacle analysis for an early warning system to be activated. Ex- periments on collected video data and comparison with other computational intelligence methods indicate promising results. This makes it possible to directly utilize available neu- romorphic hardware chips, embedded in visual prostheses, to enhance significantly their functionality. The proposed NeuCube-based obstacle avoidance methodology provides use- ful guidance to the blind, thus offering a significant improvement of current prostheses and potentially benefiting future prosthesis wearers.

Spiking neural networks

NeuCube

Simulated prosthetic vision

Visual prothesis

Obstacle avoidance

Author

Chenjie Ge

Shanghai Jiao Tong University

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Nikola Kasabov

Auckland University of Technology

Zhi Liu

Shanghai University

Jie Yang

Shanghai Jiao Tong University

Information Sciences

0020-0255 (ISSN)

Vol. 399 August 2017 30-42

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1016/j.ins.2017.03.006

More information

Latest update

6/28/2019