Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics
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 beha...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Nature Research
2024
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| _version_ | 1826313230413201408 |
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| author | Krauhausen, I Griggs, S McCulloch, I den Toonder, JMJ Gkoupidenis, P van de Burgt, Y |
| author_facet | Krauhausen, I Griggs, S McCulloch, I den Toonder, JMJ Gkoupidenis, P van de Burgt, Y |
| author_sort | Krauhausen, I |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-09-25T04:08:13Z |
| format | Journal article |
| id | oxford-uuid:f60dc3c8-a6d7-4dc6-98cc-206ff1bb9b9b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:08:13Z |
| publishDate | 2024 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | oxford-uuid:f60dc3c8-a6d7-4dc6-98cc-206ff1bb9b9b2024-06-05T20:17:10ZBio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in roboticsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f60dc3c8-a6d7-4dc6-98cc-206ff1bb9b9bEnglishJisc Publications RouterNature Research2024Krauhausen, IGriggs, SMcCulloch, Iden Toonder, JMJGkoupidenis, Pvan de Burgt, YBiological 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. |
| spellingShingle | Krauhausen, I Griggs, S McCulloch, I den Toonder, JMJ Gkoupidenis, P van de Burgt, Y Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics |
| title | Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics |
| title_full | Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics |
| title_fullStr | Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics |
| title_full_unstemmed | Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics |
| title_short | Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics |
| title_sort | bio inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics |
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