Learning Visual-Motor Cell Assemblies for the iCub Robot using a Neuroanatomically Grounded Neural Network

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Adams, S.V.; Wennekers, T.; Cangelosi, A.; Garagnani, M. and Pulvermüller, F.. 2015. 'Learning Visual-Motor Cell Assemblies for the iCub Robot using a Neuroanatomically Grounded Neural Network'. In: IEEE Symposium Series on Computational Intelligence, Cognitive Algorithms, Mind and Brain (SSCI-CCMB 2014). Orlando, United States 9-12 December 2014. [Conference or Workshop Item]

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Official URL: https://ieeexplore.ieee.org/document/7020687

Abstract or Description

In this work we describe how an existing neural model for learning Cell Assemblies (CAs) across multiple neuroanatomical brain areas has been integrated with a humanoid robot simulation to explore the learning of associations of visual and motor modalities. The results show that robust CAs are learned to enable pattern completion to select a correct motor response when only visual input is presented. We also show, with some parameter tuning and the pre-processing of more realistic patterns taken from images of real objects and robot poses the network can act as a controller for the robot in visuo-motor association tasks. This provides the basis for further neurorobotic experiments on grounded language learning.

Item Type:

Conference or Workshop Item (Paper)

Identification Number (DOI):

https://doi.org/10.1109/CCMB.2014.7020687