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Neural plasticity for rich and uncertain robotic information streams

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posted on 10.11.2016 by Andrea Soltoggio, Frank van der Velde
Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. On the contrary, natural scenarios are often rich of raw, asynchronous, overlapping and uncertain inputs and outputs whose relationships and meaning are progressively acquired, disambiguated, and used for further learning. Therefore, recent research efforts focus on neural embodied systems that rely less on well timed and pre-processed inputs, but rather extract autonomously relationships and features in time and space. In particular, realistic and more complete models of plasticity must account for delayed rewards, noisy and ambiguous data, emerging and novel input features during online learning. Such approaches model the progressive acquisition of knowledge into neural systems through experience in environments that may be affected by ambiguities, uncertain signals, delays, or novel features.

History

School

  • Science

Department

  • Computer Science

Citation

SOLTOGGIO, S. and VAN DER VELDE, F. (eds.) 2016. Neural plasticity for rich and uncertain robotic information streams. Lausanne: Frontiers Media. doi: 10.3389/978-2-88919-995-2

Publisher

© Copyright 2007-2016 Frontiers Media SA.

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VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016

Notes

Closed access. This is an edited book. It is available at: http://dx.doi.org/10.3389/978-2-88919-995-2 and the individual papers are available to download. The editorial article by Soltoggio and van der Velde is available on the Institutional Repository at: https://dspace.lboro.ac.uk/2134/19473

ISBN

978-2-88919-995-2

Language

en

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