Editorial: 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 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. The bio-inspired focus does not seek the most effective machine learning method to solve those problems, it rather points toward a better understanding of problem solving mechanisms in neural systems, which can in turn also provide viable solutions to difficult problems. Realistic models of plasticity must account for delayed rewards (Soltoggio et al., 2013a), noisy and ambiguous data (Soltoggio et al., 2013b), and emerging and novel input features during online and value learning (Krichmar and Röhrbein, 2013). Those factors have indeed been an emerging focus of search (e., with a growing number of studies that cannot be reviewed in this short editorial. Such approaches model the progressive acquisition of knowledge by neural systems through experience in environments that may be affected by ambiguities, uncertain signals, delays, or novel features fundamental properties and dynamics of neural learning systems that are naturally immersed in a rich information flow. We are pleased with the contributions collected in this Research Topic, each of which addresses key topics in this emerging and important field of research. One overarching problem in this field is that of making sense of large amounts of data from sensory systems in order to recognize particular situations and perform basic tasks. Parisi and colleagues took a self-organizing neural approach to action recognition using human pose-motion features. The Growing When Required (GWR) networks manifest a high-level structural plasticity that regulates network complexity in relation to the task (Parisi et al., 2015). Such a bio-inspired approach recorded state-of-the-art performance on a dataset of full-body actions captured with a depth sensor, with competitive results in a public benchmark of domestic daily actions. Another source of large, noisy and uncertain data is found in robotic tactile sensors. Chou et al. (2015) deployed a specific robot called CARL-SJR with a full-body tactile sensory area. CARL-SJR encourages people to communicate with …