Journal Articles
Souza, MR, Offner, G,
Soltoggio, A, Mohammad-Pour, M, Theodossiades, S (2025)
Design of electric powertrains to achieve NVH performance using autoencoders and a physical meaningful latent space,
Neural Computing and Applications, ISSN: 0941-0643.
Liu, X, Bai, Y, Lu, Y,
Soltoggio, A, Kolouri, S (2024)
Wasserstein task embedding for measuring task similarities,
Neural Networks, 181(January 2025), 106796, ISSN: 0893-6080. DOI:
10.1016/j.neunet.2024.106796.
Soltoggio, A, Ben-Iwhiwhu, E, Braverman, V, Eaton, E, Epstein, B, Ge, Y, Halperin, L, How, J, Itti, L, Jacobs, MA, Kantharaju, P, Le, L, Lee, S, Liu, X, Monteiro, ST, Musliner, D, Nath, S, Panda, P, Peridis, C, Pirsiavash, H, Parekh, V, Roy, K, Shperberg, S, Siegelmann, HT, Stone, P, Vedder, K, Wu, J, Yang, L, Zheng, G, Kolouri, S (2024)
A collective AI via lifelong learning and sharing at the edge,
Nature Machine Intelligence, 6(3), pp.251-264, DOI:
10.1038/s42256-024-00800-2.
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
Dick, J, Nath, S, Peridis, C, Benjamin, E, Kolouri, S,
Soltoggio, A (2024)
Statistical context detection for deep lifelong reinforcement learning. In
Conference on Lifelong Learning Agents (CoLLAs) 2024, Pisa, Italy.
Lu, Y, Liu, X,
Soltoggio, A, Kolouri, S (2024)
SLoSH: set locality sensitive hashing via Sliced-Wasserstein embeddings. In
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV); 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii,ISBN: 9798350318920. DOI:
10.1109/WACV57701.2024.00255.
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 Chandar, S, Pascanu, R, Sedghi, H, Precup, D (ed)
Second Conference on Lifelong Learning Agents (CoLLAs 2023); Proceedings of the 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Canada, pp.936-960.
Li, Z, Samanta, A, Li, Y,
Soltoggio, A, Kim, H, Liu, C (2023)
R3: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics. In
, Proceedings - Real-Time Systems Symposium, pp.131-144, DOI:
10.1109/RTSS59052.2023.00021.
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), pp.1-6, 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, pp.1-7, 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, DOI:
10.7551/978-0-262-32621-6-ch073.
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), pp.41-46, 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, pp.2471-2478, 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, pp.503-504, 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, pp.2165-2172, 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.
