Journal Articles

Ben-Iwhiwhu, E, Nath, S, Pilly, P, Kolouri, S, Soltoggio, A (2023) Lifelong reinforcement learning with modulating masks, Transactions on Machine Learning Research, pp.1-23.

Baker, MM, New, A, Aguilar-Simon, M, Al-Halah, Z, Arnold, SMR, Ben-Iwhiwhu, E, Brna, AP, Brooks, E, Brown, RC, Daniels, Z, Daram, A, Delattre, F, Dellana, R, Eaton, E, Fu, H, Grauman, K, Hostetler, J, Iqbal, S, Kent, C, Ketz, N, Kolouri, S, Konidaris, G, Kudithipudi, D, Learned-Miller, E, Lee, S, Littman, ML, Madireddy, S, Mendez, JA, Nguyen, EQ, Piatko, C, Pilly, PK, Raghavan, A, Rahman, A, Ramakrishnan, SK, Ratzlaff, N, Soltoggio, A, Stone, P, Sur, I, Tang, Z, Tiwari, S, Vedder, K, Wang, F, Xu, Z, Yanguas-Gil, A, Yedidsion, H, Yu, S, Vallabha, GK (2023) A domain-agnostic approach for characterization of lifelong learning systems, Neural Networks, 160, pp.274-296, ISSN: 0893-6080. DOI: 10.1016/j.neunet.2023.01.007.

Skarysz, A, Salman, D, Eddleston, M, Sykora, M, Hunsicker, E, Nailon, WH, Darnley, K, McLaren, DB, Thomas, P, Soltoggio, A (2022) Fast and automated biomarker detection in breath samples with machine learning, PLoS ONE, 17(4), e0265399, DOI: 10.1371/journal.pone.0265399.

Ben-Iwhiwhu, E, Dick, J, Ketz, NA, Pilly, PK, Soltoggio, A (2022) Context meta-reinforcement learning via neuromodulation, Neural Networks, 152, pp.70-79, ISSN: 0893-6080. DOI: 10.1016/j.neunet.2022.04.003.

Kudithipudi, D, Aguilar-Simon, M, Babb, J, Bazhenov, M, Blackiston, D, Bongard, J, Brna, AP, Raja, SC, Cheney, N, Clune, J, Daram, A, Fusi, S, Helfer, P, Kay, L, Ketz, N, Kira, Z, Kolouri, S, Krichmar, JL, Kriegman, S, Levin, M, Madireddy, S, Manicka, S, Marjaninejad, A, McNaughton, B, Miikkulainen, R, Navratilova, Z, Pandit, T, Parker, A, Pilly, PK, Risi, S, Sejnowski, TJ, Soltoggio, A, Soures, N, Tolias, AS, Urbina-Meléndez, D, Valero-Cuevas, FJ, van de Ven, GM, Vogelstein, JT, Wang, F, Weiss, R, Yanguas-Gil, A, Zou, X, Siegelmann, H (2022) Biological underpinnings for lifelong learning machines, Nature Machine Intelligence, 4(3), pp.196-210, DOI: 10.1038/s42256-022-00452-0.

Ladosz, P, Ben-Iwhiwhu, E, Dick, J, Ketz, N, Kolouri, S, Krichmar, JL, Pilly, PK, Soltoggio, A (2021) Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture, IEEE Transactions on Neural Networks and Learning Systems, ISSN: 2162-237X. DOI: 10.1109/TNNLS.2021.3110281.

Fratczak, P, Goh, Y, Kinnell, P, Justham, L, Soltoggio, A (2021) Robot apology as a post-accident trust-recovery control strategy in industrial human-robot interaction, International Journal of Industrial Ergonomics, 82, 103078, ISSN: 0169-8141. DOI: 10.1016/j.ergon.2020.103078.

Farley, S, Hodgkinson, JEA, Gordon, OM, Turner, J, Soltoggio, A, Moriarty, PJ, Hunsicker, E (2020) Improving the segmentation of scanning probe microscope images using convolutional neural networks, Machine Learning: Science and Technology, 2(1), 015015, ISSN: 2632-2153. DOI: 10.1088/2632-2153/abc81c.

Dick, J, Ladosz, P, Ben-Iwhiwhu, E, Shimadzu, H, Kinnell, P, Pilly, PK, Kolouri, S, Soltoggio, A (2020) Detecting changes and avoiding catastrophic forgetting in dynamic partially observable environments, Frontiers in Neurorobotics, 14, 578675, ISSN: 1662-5218. DOI: 10.3389/fnbot.2020.578675.

Jiang, L, Hu, Y, Xia, X, Liang, Q, Soltoggio, A, Kabir, S (2020) A multi-scale mapping approach based on a deep learning CNN model for reconstructing high-resolution urban DEMs, Water, 12(5), 1369, DOI: 10.3390/w12051369.

Alkhalifah, Y, Phillips, I, Soltoggio, A, Darnley, K, Nailon, WH, McLaren, D, Eddleston, M, Thomas, CLP, Salman, D (2019) VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography/Mass Spectrometry Data, Analytical Chemistry, 92(4), pp.2937-2945, ISSN: 0003-2700. DOI: 10.1021/acs.analchem.9b03084.

Hu, Y, Soltoggio, A, Lock, R, Carter, S (2018) A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation, Neural Networks, 109, pp.31-42, ISSN: 1879-2782. DOI: 10.1016/j.neunet.2018.10.009.

Soltoggio, A, Stanley, KO, Risi, S (2018) Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks, Neural Networks, 108, pp.48-67, ISSN: 0893-6080. DOI: 10.1016/j.neunet.2018.07.013.

Turner, J, Meng, Q, Schaefer, G, Whitbrook, A, Soltoggio, A (2017) Distributed task rescheduling with time constraints for the optimization of total task allocations in a multirobot system, IEEE Transactions on Cybernetics, 48(9), pp.2583-2597, ISSN: 2168-2267. DOI: 10.1109/TCYB.2017.2743164.

Soltoggio, A and van der Velde, F (2015) Editorial: Neural plasticity for rich and uncertain robotic information streams, Frontiers in Neurorobotics, 9(12), ISSN: 1662-5218. 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, 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 and Steil, J (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, Lemme, A, Reinhart, F, Steil, J (2013) Rare neural correlations implement robotic conditioning with delayed rewards and disturbances, Frontiers in Neurorobotics, 7(APR), DOI: 10.3389/fnbot.2013.00006.

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.

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, DOI: 10.1007/s13218-012-0192-5.

Conferences

Nath, S, Peridis, C, Ben-Iwhiwhu, E, Liu, X, Dora, S, Liu, C, Kolouri, S, Soltoggio, A (2023) Sharing lifelong reinforcement learning knowledge via modulating masks. In Second Conference on Lifelong Learning Agents (CoLLAs 2023); Proceedings of the Second Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Canada.

Krichmar, JL, Ketz, NA, Pilly, PK, Soltoggio, A (2022) Flexible Path Planning in a Spiking Model of Replay and Vicarious Trial and Error. In , pp.177-189, ISBN: 9783031167690. DOI: 10.1007/978-3-031-16770-6_15.

Fratczak, P, Goh, YM, Kinnell, P, Justham, L, Soltoggio, A (2020) Virtual Reality Study of Human Adaptability in Industrial Human-Robot Collaboration. In 2020 IEEE International Conference on Human-Machine Systems (ICHMS). DOI: 10.1109/ichms49158.2020.9209558.

Ben-Iwhiwhu, E, Ladosz, P, Dick, J, Chen, W-H, Pilly, P, Soltoggio, A (2020) Evolving inborn knowledge for fast adaptation in dynamic POMDP problems. In Genetic and Evolutionary Computation Conference (GECCO 2020); GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, Electronic-only conference, pp.280-288, ISBN: 9781450371285. DOI: 10.1145/3377930.3390214.

Kolouri, S, Ketz, NA, Pilly, PK, Soltoggio, A (2020) Sliced Cramer synaptic consolidation for preserving deeply learned representations. In International Conference On Learning Representations (ICLR 2020), Addis Abeba, Ethiopia.

Fratczak, P, Goh, YM, Kinnell, P, Soltoggio, A, Justham, L (2019) Understanding Human Behaviour in Industrial Human-Robot Interaction by Means of Virtual Reality. In HTTF 2019: Halfway to the Future, Proceedings of the Halfway to the Future Symposium 2019. DOI: 10.1145/3363384.3363403.

Turner, J, Meng, Q, Schaefer, G, Soltoggio, A (2018) Fast consensus for fully distributed multi-agent task allocation. In The 33rd ACM Symposium On Applied Computing, Pau, France,ISBN: 9781450351911. DOI: 10.1145/3167132.3167224.

Turner, J, Meng, Q, Schaefer, G, Soltoggio, A (2018) Distributed strategy adaptation with a prediction function in multi-agent task allocation. In 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, Sweden,ISBN: 9781450356497.

Mucha, A, Alkhalifah, Y, Darnley, K, Eddleston, M, Hu, Y, McLaren, DB, Nailon, WH, Salman, D, Sykora, M, Thomas, P, Soltoggio, A (2018) Convolutional neural networks for automated targeted analysis of gas chromatography-mass spectrometry data. In International Joint Conference on Neural Networks, Rio de Janeiro, Brazil,ISBN: 9781509060146. DOI: 10.1109/IJCNN.2018.8489539.

Bahroun, Y and Soltoggio, A (2017) Online representation learning with single and multi-layer Hebbian networks for image classification. In International Conference on Artificial Neural Networks, Alghero, Italy,ISBN: 9783319686110. DOI: 10.1007/978-3-319-68612-7.

Bahroun, Y, Hunsicker, E, Soltoggio, A (2017) Building efficient deep Hebbian networks for image classification tasks. In International Conference on Artificial Neural Networks, Alghero, Italy,ISBN: 9783319686110.

Bahroun, Y, Hunsicker, E, Soltoggio, A (2017) Neural networks for efficient nonlinear online clustering. In International Conference on Neural Information Processing, Guangzhou, China,ISBN: 9783319700861. DOI: 10.1007/978-3-319-70087-8_34.

Soltoggio, A, Blasing, 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: 9783319245607. 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, DOI: 10.7551/978-0-262-32621-6-ch073.

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. DOI: 10.7551/978-0-262-32621-6-ch034.

Fontana, A, Soltoggio, A, Wróbel, B (2014) POET: An evo-devo method to optimize the weights of large artificial neural networks. In , Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, pp.447-454, ISBN: 9780262326216.

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,, DOI: 10.1109/DevLrn.2013.6652572.

Soltoggio, A, Lemme, A, Steil, J (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: 978-1-4673-2125-9. DOI: 10.1109/ROBIO.2012.6491169.

Jones, BH, 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, BH (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.

Dürr, P, Mattiussi, C, Soltoggio, A, Floreano, D (2008) Evolvability of Neuromodulated Learning for Robots. In 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS). DOI: 10.1109/lab-rs.2008.22.

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

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 (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, Durr, P, Mattiussi, C, Floreano, D (2007) Evolving neuromodulatory topologies for reinforcement learning-like problems. In 2007 IEEE Congress on Evolutionary Computation. DOI: 10.1109/cec.2007.4424781.

Soltoggio, A (2006) A simple line search operator for ridged landscapes. In GECCO06: Genetic and Evolutionary Computation Conference, Proceedings of the 8th annual conference on Genetic and evolutionary computation. DOI: 10.1145/1143997.1144089.

Soltoggio, A (2005) An enhanced GA to improve the search process reliability in tuning of control systems. In GECCO05: Genetic and Evolutionary Computation Conference, Proceedings of the 7th annual conference on Genetic and evolutionary computation. 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 , pp.174-185, ISBN: 9783540223436. DOI: 10.1007/978-3-540-24855-2_16.

Books

Soltoggio, A and van der Velde, F (2016) Neural plasticity for rich and uncertain robotic information streams, © Copyright 2007-2016 Frontiers Media SA, 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. DOI: 10.1007/978-3-319-24560-7.

Chapters

Bahroun, Y, Hunsicker, E, Soltoggio, A (2017) Building Efficient Deep Hebbian Networks for Image Classification Tasks. In Artificial Neural Networks and Machine Learning – ICANN 2017, Springer International Publishing, pp.364-372, ISBN: 9783319685991. DOI: 10.1007/978-3-319-68600-4_42.

Bahroun, Y and Soltoggio, A (2017) Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification. In Artificial Neural Networks and Machine Learning – ICANN 2017, Springer International Publishing, pp.354-363, ISBN: 9783319685991. DOI: 10.1007/978-3-319-68600-4_41.

Digital/Visual Products

Soltoggio, A and Lemme, A (2013) Movement primitives as a robotic tool to interpret trajectories through learning-by-doing.

Soltoggio, A (2013) From Modulated Hebbian Plasticity to Simple Behavior Learning through Noise and Weight Saturation.

Soltoggio, A, Lemme, Andre, Reinhart, Felix, (2013) Neural learning with robot: copying with delayed rewards and disturbances.

Soltoggio, A, Lemme, Andre, Steil, Jochen, (2012) Iterative Decomposition of Complex Trajectories.

Posters

Soltoggio, A (2013) Short and long term plasticity as cause-effect hypothesis testing in robotic ambiguous scenarios.

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.

Software

Soltoggio, A, Ladosz, P, Ben-Iwhiwhu, E, Dick, J (2020) Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture, GitHub.

Hu, Y, Soltoggio, A, Lock, R, Carter, S (2018) A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation, GitHub.

Soltoggio, A (2014) Short-term plasticity as cause-effect hypothesis testing in distal reward learning, Andrea Soltoggio.

Soltoggio, A and Lemme, A, (2012) Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing, Research Institute for Cognition and Robotics.

Soltoggio, A (2012) From Modulated Hebbian Plasticity to Simple Behavior Learning through Noise and Weight Saturation - Matlab code, Andrea Soltoggio.

Soltoggio, A and Steil, Jochen, (2012) Solving the distal reward problem with rare correlations, Andrea Soltoggio.

Soltoggio, A (Accepted for publication) Matlab code: From Modulated Hebbian Plasticity to Simple Behavior Learning through Noise and Weight Saturation.

Thesis/Dissertation

Soltoggio, A (2009) Evolutionary and Computational Advantages of Neuromodulated Plasticity.

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

Datasets

Ben-Iwhiwhu, E, Dick, J, Ketz, NA, Pilly, PK, Soltoggio, A (2023) Supplementary information files for Context meta-reinforcement learning via neuromodulation, DOI: 10.17028/rd.lboro.23592483.

Turner, J, Meng, Q, Schaefer, G, Whitbrook, A, Soltoggio, A (2018) Supplementary files for article Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System, DOI: 10.17028/rd.lboro.5993038.

Other

Soltoggio, A, Stanley, KO, Risi, S (2018) Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks. DOI: 10.1016/j.neunet.2018.07.013.