Neural Plasticity for Rich and Uncertain Robotic Information Streams
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 sc...
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| Formato: | Online |
| Idioma: | inglês |
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Frontiers Media SA
2021
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| Acesso em linha: | 18382 |
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| _version_ | 1869514512374169600 |
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| author | Andrea Soltoggio Frank van der Velde |
| author_browse | Andrea Soltoggio Frank van der Velde |
| author_facet | Andrea Soltoggio Frank van der Velde |
| author_sort | Andrea Soltoggio |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-54480 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media SA |
| publisherStr | Frontiers Media SA |
| record_format | ojs |
| spelling | doab-20.500.12854ir-544802024-04-05T12:35:23Z Neural Plasticity for Rich and Uncertain Robotic Information Streams Andrea Soltoggio Frank van der Velde RC321-571 Q1-390 Neuro-robotics emobodied cognition neural plasticity Neural adaptation Cognitive Modeling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences 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. 2021-02-11T20:48:19Z 2021-02-11T20:48:19Z 2016-01-19 14:05:46 2016 book 18382 16648714 9782889199952 https://directory.doabooks.org/handle/20.500.12854/54480 eng Frontiers Research Topics image/jpeg Attribution 4.0 International http://www.frontiersin.org/books/Neural_Plasticity_for_Rich_and_Uncertain_Robotic_Information_Streams/1043 http://journal.frontiersin.org/researchtopic/3107/neural-plasticity-for-rich-and-uncertain-robotic-information-streams Frontiers Media SA 10.3389/978-2-88919-995-2 10.3389/978-2-88919-995-2 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889199952 83 open access |
| spellingShingle | RC321-571 Q1-390 Neuro-robotics emobodied cognition neural plasticity Neural adaptation Cognitive Modeling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences Andrea Soltoggio Frank van der Velde Neural Plasticity for Rich and Uncertain Robotic Information Streams |
| title | Neural Plasticity for Rich and Uncertain Robotic Information Streams |
| title_full | Neural Plasticity for Rich and Uncertain Robotic Information Streams |
| title_fullStr | Neural Plasticity for Rich and Uncertain Robotic Information Streams |
| title_full_unstemmed | Neural Plasticity for Rich and Uncertain Robotic Information Streams |
| title_short | Neural Plasticity for Rich and Uncertain Robotic Information Streams |
| title_sort | neural plasticity for rich and uncertain robotic information streams |
| topic | RC321-571 Q1-390 Neuro-robotics emobodied cognition neural plasticity Neural adaptation Cognitive Modeling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| topic_facet | RC321-571 Q1-390 Neuro-robotics emobodied cognition neural plasticity Neural adaptation Cognitive Modeling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| url | 18382 |
| work_keys_str_mv | AT andreasoltoggio neuralplasticityforrichanduncertainroboticinformationstreams AT frankvandervelde neuralplasticityforrichanduncertainroboticinformationstreams |