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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/23124

Title: Neural plasticity for rich and uncertain robotic information streams
Authors: Soltoggio, Andrea
van der Velde, Frank
Issue Date: 2016
Publisher: © Copyright 2007-2016 Frontiers Media SA.
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
Abstract: 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.
Description: 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
Version: Published
DOI: 10.3389/978-2-88919-995-2
URI: https://dspace.lboro.ac.uk/2134/23124
Publisher Link: http://dx.doi.org/10.3389/978-2-88919-995-2
ISBN: 978-2-88919-995-2
Appears in Collections:Closed Access (Computer Science)

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