Loughborough University
frobt-08-732023.pdf (6.29 MB)

Multimodal representation learning for place recognition using deep hebbian predictive coding

Download (6.29 MB)
journal contribution
posted on 2022-03-04, 10:57 authored by Martin J Pearson, Shirin DoraShirin Dora, Oliver Struckmeier, Thomas C Knowles, Ben Mitchinson, Kshitij Tiwari, Ville Kyrki, Sander Bohte, Cyriel MA Pennartz
Recognising familiar places is a competence required in many engineering applications that interact with the real world such as robot navigation. Combining information from different sensory sources promotes robustness and accuracy of place recognition. However, mismatch in data registration, dimensionality, and timing between modalities remain challenging problems in multisensory place recognition. Spurious data generated by sensor drop-out in multisensory environments is particularly problematic and often resolved through adhoc and brittle solutions. An effective approach to these problems is demonstrated by animals as they gracefully move through the world. Therefore, we take a neuro-ethological approach by adopting self-supervised representation learning based on a neuroscientific model of visual cortex known as predictive coding. We demonstrate how this parsimonious network algorithm which is trained using a local learning rule can be extended to combine visual and tactile sensory cues from a biomimetic robot as it naturally explores a visually aliased environment. The place recognition performance obtained using joint latent representations generated by the network is significantly better than contemporary representation learning techniques. Further, we see evidence of improved robustness at place recognition in face of unimodal sensor drop-out. The proposed multimodal deep predictive coding algorithm presented is also linearly extensible to accommodate more than two sensory modalities, thereby providing an intriguing example of the value of neuro-biologically plausible representation learning for multimodal navigation.


European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3)



  • Science


  • Computer Science

Published in

Frontiers in Robotics and AI




Frontiers Media


  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Frontiers Media under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date


Publication date


Copyright date





  • en


Dr Shirin Dora. Deposit date: 3 March 2022

Article number