LEADER 02208nam  2200361z- 450 
001 9910688347003321
005 20231214132932.0
035   $a(CKB)3800000000216301
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54480
035   $a(EXLCZ)993800000000216301
100   $a20202102d2016      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aNeural Plasticity for Rich and Uncertain Robotic Information Streams
210   $cFrontiers Media SA$d2016
215   $a1 electronic resource (83 p.)
225 1 $aFrontiers Research Topics
311   $a2-88919-995-9 
330   $aModels 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.
610   $aNeuro-robotics
610   $aemobodied cognition
610   $aneural plasticity
610   $aNeural adaptation
610   $aCognitive Modeling
700   $aAndrea Soltoggio$4auth$01352644
702   $aFrank van der Velde$4auth
906   $aBOOK
912   $a9910688347003321
996   $aNeural Plasticity for Rich and Uncertain Robotic Information Streams$93191039
997   $aUNINA