Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Imke Krauhausen; Dimitrios A. Koutsouras; Armantas Melianas; Scott T. Keene; Katharina Lieberth; Hadrien Ledanseur; Rajendar Sheelamanthula; Alexander Giovannitti; Fabrizio Torricelli; Iain McCulloch; Paul W.M. Blom; Alberto Salleo; Yoeri van de Burgt; Paschalis Gkoupidenis

doi:10.1126/sciadv.abl5068

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Imke Krauhausen, Dimitrios A. Koutsouras, Armantas Melianas, Scott T. Keene, Katharina Lieberth, Hadrien Ledanseur, Rajendar Sheelamanthula, Alexander Giovannitti, Fabrizio Torricelli, Iain McCulloch, Paul W.M. Blom, Alberto Salleo, Yoeri van de Burgt, Paschalis Gkoupidenis

Research output: Contribution to journal › Article › peer-review

89 Scopus citations

Abstract

In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

Original language	English (US)
Article number	eabl5068
Journal	Science Advances
Volume	7
Issue number	50
DOIs	https://doi.org/10.1126/sciadv.abl5068
State	Published - Dec 2021
Externally published	Yes

All Science Journal Classification (ASJC) codes

General

Access to Document

10.1126/sciadv.abl5068

Cite this

Krauhausen, I., Koutsouras, D. A., Melianas, A., Keene, S. T., Lieberth, K., Ledanseur, H., Sheelamanthula, R., Giovannitti, A., Torricelli, F., McCulloch, I., Blom, P. W. M., Salleo, A., de Burgt, Y. V., & Gkoupidenis, P. (2021). Organic neuromorphic electronics for sensorimotor integration and learning in robotics. Science Advances, 7(50), Article eabl5068. https://doi.org/10.1126/sciadv.abl5068

@article{bcd4fbc326874fed822fcb80c07f8646,

title = "Organic neuromorphic electronics for sensorimotor integration and learning in robotics",

abstract = "In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.",

author = "Imke Krauhausen and Koutsouras, {Dimitrios A.} and Armantas Melianas and Keene, {Scott T.} and Katharina Lieberth and Hadrien Ledanseur and Rajendar Sheelamanthula and Alexander Giovannitti and Fabrizio Torricelli and Iain McCulloch and Blom, {Paul W.M.} and Alberto Salleo and {de Burgt}, {Yoeri van} and Paschalis Gkoupidenis",

note = "Publisher Copyright: Copyright {\textcopyright} 2021 The Authors, some rights reserved;",

year = "2021",

month = dec,

doi = "10.1126/sciadv.abl5068",

language = "English (US)",

volume = "7",

journal = "Science Advances",

issn = "2375-2548",

publisher = "American Association for the Advancement of Science",

number = "50",

}

Krauhausen, I, Koutsouras, DA, Melianas, A, Keene, ST, Lieberth, K, Ledanseur, H, Sheelamanthula, R, Giovannitti, A, Torricelli, F, McCulloch, I, Blom, PWM, Salleo, A, de Burgt, YV & Gkoupidenis, P 2021, 'Organic neuromorphic electronics for sensorimotor integration and learning in robotics', Science Advances, vol. 7, no. 50, eabl5068. https://doi.org/10.1126/sciadv.abl5068

TY - JOUR

T1 - Organic neuromorphic electronics for sensorimotor integration and learning in robotics

AU - Krauhausen, Imke

AU - Koutsouras, Dimitrios A.

AU - Melianas, Armantas

AU - Keene, Scott T.

AU - Lieberth, Katharina

AU - Ledanseur, Hadrien

AU - Sheelamanthula, Rajendar

AU - Giovannitti, Alexander

AU - Torricelli, Fabrizio

AU - McCulloch, Iain

AU - Blom, Paul W.M.

AU - Salleo, Alberto

AU - de Burgt, Yoeri van

AU - Gkoupidenis, Paschalis

PY - 2021/12

Y1 - 2021/12

N2 - In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

AB - In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

UR - http://www.scopus.com/inward/record.url?scp=85121137483&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85121137483&partnerID=8YFLogxK

U2 - 10.1126/sciadv.abl5068

DO - 10.1126/sciadv.abl5068

M3 - Article

C2 - 34890232

AN - SCOPUS:85121137483

SN - 2375-2548

VL - 7

JO - Science Advances

JF - Science Advances

IS - 50

M1 - eabl5068

ER -

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this