Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Research Publications


Publications for Andrea Soltoggio

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Journal Articles

Soltoggio, A and van der Velde, F (2015) Neural plasticity for rich and uncertain robotic information streams, Frontiers in Neurorobotics, 9(12), ISSN: 1662-5218. Full text: http://journal.frontiersin.org/article/10.3389/fnbot.2015.00012/full. DOI: 10.3389/fnbot.2015.00012.

Soltoggio, A (2014) Short-term plasticity as cause-effect hypothesis testing in distal reward learning, Biological Cybernetics, 109(1), pp.75-94, Full text: http://link.springer.com/article/10.1007/s00422-014-0628-0#page-1. DOI: 10.1007/s00422-014-0628-0.

Soltoggio, A and Lemme, A (2013) Movement primitives as a robotic tool to interpret trajectories through learning-by-doing, International Journal of Automation and Computing, 10(5), pp.375-386, ISSN: 1476-8186. DOI: 10.1007/s11633-013-0734-9.

Soltoggio, A, Lemme, A, Reinhart, F, Steil, J (2013) Rare neural correlations implement robotic conditioning with delayed rewards and disturbances, Frontiers in Neurorobotics, Frontiers in Neurorobotics, 6, pp.1-18, DOI: 10.3389/fnbot.2013.00006.

Soltoggio, A and Steil, JJ (2013) Solving the distal reward problem with rare correlations, Neural Computation, 25(4), pp.940-978, ISSN: 0899-7667. DOI: 10.1162/NECO_a_00419.

Soltoggio, A and Steil, J (2012) How Rich Motor Skills Empower Robots at Last: Insights and Progress of the AMARSi Project, Kuenstlich Intelligenz, 26(4), pp.407-410.

Soltoggio, A and Stanley, KO (2012) From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation, Neural Networks, 34, pp.28-41, ISSN: 0893-6080. DOI: 10.1016/j.neunet.2012.06.005.



Conferences

Bahroun, Hunsicker, Soltoggio, A (Accepted for publication) Building Efficient Deep Hebbian Networks for Image Classification Tasks. In International Conference on Artificial Neural Networks, Alghero, Italy.

Bahroun, Y and Soltoggio, A (Accepted for publication) Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification Tasks. In International Conference on Artificial Neural Networks, Alghero, Italy.

Soltoggio, A, Blaesing, B, Moscatelli, A, Schack, T (2015) The Aikido inspiration to safety and efficiency: an investigation on forward roll impact forces. In 10th International Symposium on Computer Science in Sports, Loughborough, UK, pp.119-127, ISBN: 978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7_15.

Fontana, A, Soltoggio, A, Wrobel, B (2014) POET: an evo-devo method to optimize the weights of a large artificial neural networks. In Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XIV). Cambridge, MA: MIT Press, 2014, New York, USA, pp.1-8.

Pugh, JK, Soltoggio, A, Stanley, KO (2014) Real-time Hebbian Learning from Autoencoder Features for Control Tasks. In Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XIV), NYC, USA.

Soltoggio, A, Reinhart, F, Lemme, A, Steil, J (2013) Learning the rules of a game: Neural conditioning in human-robot interaction with delayed rewards. In , 2013 IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL 2013 - Electronic Conference Proceedings,ISBN: 9781479910366. DOI: 10.1109/DevLrn.2013.6652572.

Soltoggio, A, Lemme, A, Steil, JJ (2012) Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing. In , 2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012 - Conference Digest, pp.1427-1433, ISBN: 9781467321273. DOI: 10.1109/ROBIO.2012.6491169.

Ben, J, Soltoggio, A, Sendhoff, B, Yao, X (2011) Evolution of neural symmetry and its coupled alignment to body plan morphology. In , Genetic and Evolutionary Computation Conference, GECCO'11, pp.235-242, ISBN: 9781450305570. DOI: 10.1145/2001576.2001609.

Soltoggio, A and Jones, B (2009) Novelty of behaviour as a basis for the neuro-evolution of operant reward learning. In , Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, pp.169-176, ISBN: 9781605583259. DOI: 10.1145/1569901.1569925.

Soltoggio, A (2008) Phylogenetic Onset and Dynamics of Neuromodulation in Learning Neural Models. In Young Physiologists’ Symposium, Cambridge, UK, pp.1-1.

Dürr, P, Mattiussi, C, Soltoggio, A, Floreano, D (2008) Evolvability of neuromodulated learning for robots. In , Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008, pp.41-46, ISBN: 9780769532721. DOI: 10.1109/LAB-RS.2008.22.

Soltoggio, A (2008) Neural plasticity and minimal topologies for reward-based learning. In , Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008, pp.637-642, ISBN: 9780769533261. DOI: 10.1109/HIS.2008.155.

Soltoggio, A, Bullinaria, JA, Mattiussi, C, Dürr, P, Floreano, D (2008) Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios. In Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, Winchester, UK, pp.569-576, ISBN: 978-0-262-28719-7.

Soltoggio, A, Dürr, P, Mattiussi, C, Floreano, D (2007) Evolving neuromodulatory topologies for reinforcement learning-like problems. In , 2007 IEEE Congress on Evolutionary Computation, CEC 2007, pp.2471-2478, ISBN: 1424413400. DOI: 10.1109/CEC.2007.4424781.

Soltoggio, A (2006) A simple line search operator for ridged landscapes. In , GECCO 2006 - Genetic and Evolutionary Computation Conference, pp.503-504, ISBN: 1595931864.

Soltoggio, A (2005) An enhanced GA to improve the search process reliability in tuning of control systems. In , GECCO 2005 - Genetic and Evolutionary Computation Conference, pp.2165-2172, ISBN: 1595930108. DOI: 10.1145/1068009.1068365.

Soltoggio, A (2004) GP and GA in the design of a constrained control system with disturbance rejection. In , IEEE International Symposium on Intelligent Control - Proceedings, pp.477-482.

Soltoggio, A (2004) A comparison of genetic programming and genetic algorithms in the design of a robust, saturated control system. In , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp.174-185.



Books

Soltoggio, A and van der Velde, F, (ed) (2016) Neural Plasticity for Rich and Uncertain Robotic Information Streams,ISBN: 978-2-88919-995-2. DOI: 10.3389/978-2-88919-995-2.

Soltoggio, A and van der Velde, F, (ed) (2016) Neural Plasticity for Rich and Uncertain Robotic Information Streams,ISBN: 978-2-88919-995-2. DOI: 10.3389/978-2-88919-995-2.

Chung, P, Soltoggio, A, Dawson, C, Meng, Q, Pain, M (ed) (2015) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer, ISBN: 978-3-319-24558-4. Full text: http://link.springer.com/book/10.1007/978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7.

Chung, P, Soltoggio, A, Dawson, C, Meng, Q, Pain, M (ed) (2015) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer, ISBN: 978-3-319-24558-4. Full text: http://link.springer.com/book/10.1007/978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7.



Other

Soltoggio, A, Stanley, KO, Risi, S Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks, Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence, but the complexity of the whole system of interactions is an obstacle to the understanding of the key factors at play. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks, artificial systems composed of sensors, outputs, and plastic components that change in response to sensory-output experiences in an environment. These systems may reveal key algorithmic ingredients of adaptation, autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed structures and algorithms currently used in most deep neural networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main computational methods and results are reviewed. Finally, new opportunities and developments are presented..



Posters

Soltoggio, A (2013) Short and long term plasticity as cause-effect hypothesis testing in robotic ambiguous scenarios, Bernstein Sparks Workshop. NeuroEnginneering the Brain: from Neuroscience to Robotics ..and back.



Reports

Soltoggio, A, Steil, Jochen, Kappel, David, Pecevski, Dejan, Rueckert, Elmar, Maass, Wolfgang, (2014) Technical report on Meta-learning Approaches - Adaptive Modular Architectures for Rich Motor Skills (ICT-248311), European Union.

Soltoggio, A (2003) A Case Study of a Genetically Evolved Control System, Norwegian University of Science and Technology.



Thesis/Dissertation

Soltoggio, A (2008) Evolutionary and Computational Advantages of Neuromodulated Plasticity.

Soltoggio, A (2004) Evolutionary Algorithms in the Design and Tuning of a Control System.



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