Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Imke Krauhausen, Sophie Griggs, Iain McCulloch, Jaap M.J. den Toonder, Paschalis Gkoupidenis, Yoeri van de Burgt

Research output: Contribution to journalArticlepeer-review

Abstract

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Original languageEnglish (US)
Article number4765
JournalNature communications
Volume15
Issue number1
DOIs
StatePublished - Dec 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

Fingerprint

Dive into the research topics of 'Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics'. Together they form a unique fingerprint.

Cite this