Editorial: Neural plasticity for rich and uncertain robotic information streams.

Andrea Soltoggio,Frank Van Der Velde

doi:10.3389/fnbot.2015.00012

Andrea Soltoggio, Frank Van Der Velde

Open Access

https://doi.org/10.3389/fnbot.2015.00012

Copy DOI

Abstract

Editorial: Neural plasticity for rich and uncertain robotic information streams.

Highlights

Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules
The bio-inspired focus does not seek the most effective machine learning method to solve those problems, it rather points toward a better understanding of problem solving mechanisms in neural systems, which can in turn provide viable solutions to difficult problems
Motor control is a problem in which neural plasticity results in high level of adaptation, adjusting neural systems to operate in combination with specific bio-mechanical structures and morphologies. (Burms et al, 2015) demonstrated the utility of modulated Hebbian plasticity in embodied computation for compliant robotics

Summary

Introduction

Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. Recent research efforts focus on neural embodied systems that rely less on well timed and pre-processed inputs, but rather extract autonomously relationships and features in time and space. The bio-inspired focus does not seek the most effective machine learning method to solve those problems, it rather points toward a better understanding of problem solving mechanisms in neural systems, which can in turn provide viable solutions to difficult problems.

Results

Conclusion