Soltoggio, A.

Van, der Velde F.

English

Leiden University Scholary Publications

EDITORIALpublished: 27 October 2015doi: 10.3389/fnbot.2015.00012Frontiers in Neurorobotics | www.frontiersin.org 1 October 2015 | Volume 9 | Article 12Edited by:Florian Röhrbein,Technische Universität München,GermanyReviewed by:Georg Martius,Institute for Science and TechnologyAustria, Austria*Correspondence:Andrea Soltoggioa.soltoggio@lboro.ac.ukReceived: 03 September 2015Accepted: 08 October 2015Published: 27 October 2015Citation:Soltoggio A and van der Velde F(2015) Editorial: Neural plasticity forrich and uncertain robotic informationstreams. Front. Neurorobot. 9:12.doi: 10.3389/fnbot.2015.00012Editorial: Neural plasticity for richand uncertain robotic informationstreamsAndrea Soltoggio 1* and Frank van der Velde 21Computer Science, Loughborough University, Loughborough, UK, 2CTIT, CPE-BMS, University of Twente, Enschede,NetherlandsKeywords: neural plasticity, neural adaptation, neuro-robotics, cognitive modeling, emobodied cognitionModels of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavilypre-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 andoutputs whose relationships and meaning are progressively acquired, disambiguated, and used forfurther learning. Therefore, recent research efforts focus on neural embodied systems that relyless on well timed and pre-processed inputs, but rather extract autonomously relationships andfeatures in time and space. The bio-inspired focus does not seek themost effective machine learningmethod to solve those problems, it rather points toward a better understanding of problem solvingmechanisms 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), noisyand ambiguous data (Soltoggio et al., 2013b), and emerging and novel input features during onlineand value learning (Krichmar and Röhrbein, 2013). Those factors have indeed been an emergingfocus of search (e.g., Sporns and Alexander, 2003; Lungarella and Sporns, 2006; Martius et al.,2013), with a growing number of studies that cannot be reviewed in this short editorial. Suchapproaches model the progressive acquisition of knowledge by neural systems through experiencein environments that may be affected by ambiguities, uncertain signals, delays, or novel features(Pugh et al., 2014; Soltoggio, 2015). This Research Topic in Frontiers in Neurorobotics exploredfundamental properties and dynamics of neural learning systems that are naturally immersed in arich information flow. We are pleased with the contributions collected in this Research Topic, eachof 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 fromsensory systems in order to recognize particular situations and perform basic tasks. Parisi andcolleagues took a self-organizing neural approach to action recognition using human pose-motionfeatures. The Growing When Required (GWR) networks manifest a high-level structural plasticitythat regulates network complexity in relation to the task (Parisi et al., 2015). Such a bio-inspiredapproach recorded state-of-the-art performance on a dataset of full-body actions captured with adepth 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 it through gentle touch, and provides feedback tousers by displaying bright colors on its surface. The time-delayed and uncertain nature of theinteractions poses challenges to the formation of correct associations between stimuli, rewardsand actions. The approach devised by Chou et al. (2015) experiments with a strongly bio-inspiredarchitecture of spiking neurons with neuromodulated plasticity. The model abstracts brain areassuch as the primary somatosensory cortex, prefrontal cortex, striatum, and the insular cortex toprocess noisy data generated directly from CARL-SJR’s tactile sensory area. The result is a robustlearning mechanism that reliably forms correct associations and preferences for directions withoutheavily pre-processed inputs.Soltoggio and van der Velde Neural plasticity for robotic information streamsUncertainty and large amount of data are also found incollaborative multi-robot scenarios in which multiple robotswork alongside humans. Galbraith and colleagues propose amotor babbling approach to learn a complex set of relationsand interactions with the 11-degrees-of-freedom RoPro Calliopemobile robot (Galbraith et al., 2015). Motor babbling of itswheels and arm enabled the Calliope to learn how to relatevisual and proprioceptive information to achieve hand-eye-bodycoordination.Motor control is a problem in which neural plasticity resultsin high level of adaptation, adjusting neural systems to operatein combination with specific bio-mechanical structures andmorphologies. (Burms et al., 2015) demonstrated the utilityof modulated Hebbian plasticity in embodied computationfor compliant robotics. In such scenarios, control policies aregenerally unknown due to the partial offload of control policiesto morphological computation. Modulated Hebbian plasticitywas shown to lead to hybrid controllers that naturally integratethe computations that are performed by the robot’s body intoa neural network architecture. Those results demonstrate thepotential of universal applicability of plasticity rules to complexcontrol problems.A similar problem was tackled in Dasgupta et al. (2015)in which they used distributed recurrent neural networkswith synaptic adaptation to find a range of complexbehaviors for walking robots. In particular, their approachdemonstrated a remarkable flexibility in designing controlsystems that can work with multi-legged robots. A CentralPattern Generator is used to feed a self-adaptive reservoirnetwork, which in turn provides motor control througha read-out integration unit. These results contribute todemonstrate the efficacy and continuous advancementof plastic neural models in complex input-output controlscenarios.The overall vision provided by these research papers outlinesan increasingly more effective deployment of plastic neuralmodels to tackle complex perception and control problems inwhich noise, uncertainty and delays pose a challenge to manyalgorithms. This vision matches the intuition of bioinspiredneurorobotics approaches that propose advanced, plastic neuralsystems as viable models when sensory-motor information flowsapproach the richness and complexity found in the behavior ofbiological systems. We foresee a continuous growing attentionto this emerging research area, in particular related to thedevelopment of more effective, scalable and general neurallearning algorithms to effectively tackle rich and uncertainrobotic information streams.AUTHOR CONTRIBUTIONSAS devised the structure. Both authors AS and FV formulated thecontent and wrote the paper.REFERENCESBurms, J., Caluwaerts, K., and Dambre, J. (2015). Reward modulated hebbianplasticity as leverage for partially embodied control in compliant robotics.Front. Neurorobot. 9:9. doi: 10.3389/fnbot.2015.00009Chou, T.-S., Bucci, L. D., and Krichmar, J. L. (2015). Learning touch preferenceswith a tactile robot using dopamine modulated stdp in a model of insularcortex. Front. Neurorobot. 9:6. doi: 10.3389/fnbot.2015.00006Dasgupta, S., Goldschmidt, D., Wörgötter, F., and Manoonpong, P. (2015).Distributed recurrent neural forward models with synaptic adaptationand CPG-based control for complex behaviors of walking robots. Front.Neurorobot. 9:10. doi: 10.3389/fnbot.2015.00010Galbraith, B. V., Guenther, F. H., and Versace, M. (2015). A neural network-based exploratory learning and motor planning system for co-robots. Front.Neurorobot. 9:7. doi: 10.3389/fnbot.2015.00007Krichmar, J. L., and Röhrbein, F. (2013). Value and reward based learning inneurorobots. Front. Neurorobot. 7:13. doi: 10.3389/fnbot.2013.00013Lungarella, M., and Sporns, O. (2006). Mapping information flow in sensorimotornetworks. PLoS Comput. Biol. 2:e144. doi: 10.1371/journal.pcbi.0020144Martius, G., Der, R., and Ay, N. (2013). Information driven self-organization of complex robotic behaviors. PLoS ONE 8:e63400. doi:10.1371/journal.pone.0063400Parisi, G. I., Weber, C., and Wermter, S. (2015). Self-organizing neural integrationof pose-motion features for human action recognition. Front. Neurorobot. 9:3.doi: 10.3389/fnbot.2015.00003Pugh, J. K., Soltoggio, A. and Stanley, K. O. (2014). “Real-time hebbian learningfrom autoencoder features for control tasks,” in Artificial Life 14: Proceedingsof the Fourteenth International Conference on the Synthesis and Simulation ofLiving Systems (ALIFE XIV) (Cambridge, MA: MIT Press), 202–209.Soltoggio, A. (2015). Short-term plasticity as cause-effect hypothesis testing indistal reward learing. Biol. Cybern. 109, 75–94.Soltoggio, A., Lemme, A., Reinhart, F. R., and Steil, J. J. (2013a). Rareneural correlations implement robotic conditioning with reward delaysand disturbances. Front. Neurorobot. 7:6. doi: 10.3389/fnbot.2013.00006Soltoggio, A., Reinhart, F. R., Lemme, A., and Steil, J. J. (2013b). “Learning therules of a game: neural conditioning in human-robot interaction with delayedrewards,” in Proceedings of the Third Joint IEEE International Conference onDevelopment and Learning and on Epigenetic Robotics (Osaka).Sporns, O., and Alexander, W. H. (2003). “Neuromodulation in a learning robot:interactions between neural plasticity and behavior,” in Neural Networks, 2003.Proceedings of the International Joint Conference on, Vol. 4 (IEEE), 2789–2794.doi: 10.1109/IJCNN.2003.1224010Conflict of Interest Statement: The authors declare that the research wasconducted in the absence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.Copyright © 2015 Soltoggio and van der Velde. This is an open-access articledistributed under the terms of the Creative Commons Attribution License (CC BY).The use, distribution or reproduction in other forums is permitted, provided theoriginal author(s) or licensor are credited and that the original publication in thisjournal is cited, in accordance with accepted academic practice. No use, distributionor reproduction is permitted which does not comply with these terms.Frontiers in Neurorobotics | www.frontiersin.org 2 October 2015 | Volume 9 | Article 12

Neural plasticity for rich and uncertain robotic information streams

https://scholarlypublications.universiteitleiden.nl/access/item%3A3139893/download

Neural plasticity for rich and uncertain robotic information streams

Abstract

Similar works

Full text

Available Versions

Leiden University Scholary Publications