Dr Sergio Davies MSc Eng, PhD, MIET, MIEEE, FHEA
Dr Sergio Davies is a senior lecturer in the department of computing at Sheffield Hallam University, teaching fundamentals of computing, embedded architectures and parallel computing. Sergio's research focuses on spiking neural networks, and participated in the SpiNNaker project and the Human Brain Project (HBP). He is now focusing on applications of spiking neural networks for human-robot interactions. Sergio has also large experience in industry as he worked as strategic IT consultant, and as project leader in the fields of computer hardware, software development, embedded platforms and computer networking. Sergio is a member of the IET and of the IEEE, and he is a fellow of the HEA.
Dr Sergio Davies is a senior lecturer in the department of computing at Sheffield Hallam University. After graduating (equivalent to MSc) in telecommunication engineering at the university "Federico II" in Naples, Italy, he worked for two years as IT consultant at KPMG in Rome, Italy. After this, he received a scolarship at the University of Manchester, where he received his Ph.D. in 2012 in computer science, working on the SpiNNaker project. Sergio then received an offer to continue his research work on spiking neural networks as a post-doc within the Human Brain Project (HBP), until 2016. After this he moved to industry to lead research projects and teams in the fields of computer hardware, software development and computer networking. Sergio returned to academia in 2019 at Sheffield Hallam University to research on applications of spiking neural networks for human-robot interactions.
College of Business, Technology and Engineering
Games and Artificial Intelligence.
- BSc Computer Science For Games
- BSc Computer Science
- BSc Software engineering
- BSc Computing
- MSc Healthcare Analytics and Artificial Intelligence
- MSc Artificial Intelligence
- Concurrent and Parallel Systems (CaPS)
- Programming "Things"
- Fundamentals of Computer Architecture (FoCA)
- Smart Interactive Healthcare Technologies
- Deep Neural Networks and Learning Systems
- Project "NUMBERS"
- Project "PERSEO"
Collaboration with the Smart Interactive Technologies (SIT) Research Laboratory
Aitsam, M., Davies, S., & Di Nuovo, A. (2022). Neuromorphic Computing for Interactive Robotics: A Systematic Review. IEEE Access, 10. http://doi.org/10.1109/access.2022.3219440
Davies, S., Lucas, A., Ricolfe-Viala, C., & Di Nuovo, A. (2021). A Database for Learning Numbers by Visual Finger Recognition in Developmental Neuro-Robotics. Frontiers in Neurorobotics, 15. http://doi.org/10.3389/fnbot.2021.619504
Rast, A.D., Adams, S.V., Davidson, S., Davies, S., Hopkins, M., Rowley, A., ... Cangelosi, A. (2018). Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach. IEEE Transactions on Neural Networks and Learning Systems, 29 (12), 6132-6144. http://doi.org/10.1109/tnnls.2018.2816518
Davies, S., Galluppi, F., Rast, A.D., & Furber, S.B. (2012). A forecast-based STDP rule suitable for neuromorphic implementation. Neural networks : the official journal of the International Neural Network Society, 32, 3-14. http://doi.org/10.1016/j.neunet.2012.02.018
Rast, A., Galluppi, F., Davies, S., Plana, L., Patterson, C., Sharp, T., ... Furber, S. (2011). Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware. Neural networks : the official journal of the International Neural Network Society, 24 (9), 961-978. http://doi.org/10.1016/j.neunet.2011.06.014
Davies, S., Patterson, C., Galluppi, F., Rast, A.D., Lester, D., & Furber, S.B. (2010). Interfacing real-time spiking I/O with the SpiNNaker neuromimetic architecture. Australian Journal of Intelligent Information Processing Systems, 11, 7-11. http://cs.anu.edu.au/ojs/index.php/ajiips/article/view/1071
Xin Jin, , Lujan, M., Plana, L.A., Davies, S., Temple, S., & Furber, S.B. (2010). Modeling Spiking Neural Networks on SpiNNaker. Computing in Science & Engineering, 12 (5), 91-97. http://doi.org/10.1109/mcse.2010.112
Siino, A., Barchi, F., Davies, S., Urgese, G., & Acquaviva, A. (2016). Data and Commands Communication Protocol for Neuromorphic Platform Configuration. 2016 IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSOC). http://doi.org/10.1109/mcsoc.2016.41
Rast, A.D., Stokes, A.B., Davies, S., Adams, S.V., Akolkar, H., Lester, D.R., ... Furber, S. (2015). Transport-Independent Protocols for Universal AER Communications. Neural Information Processing, 9492 (9492), 675-684.
Davies, S., Stewart, T., Eliasmith, C., & Furber, S. (2014). Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator. The 2013 International Joint Conference on Neural Networks (IJCNN). http://doi.org/10.1109/ijcnn.2013.6706962
Davies, S., Navaridas, J., Galluppi, F., & Furber, S. (2012). Population-based routing in the SpiNNaker neuromorphic architecture. The 2012 International Joint Conference on Neural Networks (IJCNN). http://doi.org/10.1109/ijcnn.2012.6252635
Galluppi, F., Davies, S., Furber, S., Stewart, T., & Eliasmith, C. (2012). Real time on-chip implementation of dynamical systems with spiking neurons. The 2012 International Joint Conference on Neural Networks (IJCNN). http://doi.org/10.1109/ijcnn.2012.6252706
Galluppi, F., Davies, S., Rast, A., Sharp, T., Plana, L.A., & Furber, S. (2012). A hierachical configuration system for a massively parallel neural hardware platform. Proceedings of the 9th conference on Computing Frontiers. http://doi.org/10.1145/2212908.2212934
Webb, A., Davies, S., & Lester, D. (2011). Spiking Neural PID Controllers. In Neural Information Processing, (pp. 259-267). Springer Berlin Heidelberg: http://doi.org/10.1007/978-3-642-24965-5_28
Davies, S., Rast, A.D., Galluppi, F., & Furber, S.B. (2011). Maintaining Real-Time Synchrony on SpiNNaker. PROCEEDINGS OF THE 2011 8TH ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS (CF 2011). http://doi.org/10.1145/2015604.2016522
Davies, S., Rast, A., Galluppi, F., & Furber, S. (2011). A forecast-based biologically-plausible STDP learning rule. The 2011 International Joint Conference on Neural Networks. http://doi.org/10.1109/ijcnn.2011.6033444
Rast, A., Galluppi, F., Davies, S., Plana, L.A., Sharp, T., & Furber, S. (2011). An event-driven model for the SpiNNaker virtual synaptic channel. The 2011 International Joint Conference on Neural Networks. http://doi.org/10.1109/ijcnn.2011.6033466
Davies, S., Rast, A.D., Galluppi, F., & Furber, S.B. (2011). Maintaining real-time synchrony on SpiNNaker. Proceedings of the 8th ACM International Conference on Computing Frontiers. http://doi.org/10.1145/2016604.2016622
Galluppi, F., Rast, A., Davies, S., & Furber, S. (2010). A General-Purpose Model Translation System for a Universal Neural Chip. Lecture Notes in Computer Science, vol 6443, 58-65. http://doi.org/10.1007/978-3-642-17537-4_8
Jin, X., Galluppi, F., Patterson, C., Rast, A., Davies, S., Temple, S., & Furber, S. (2010). Algorithm and software for simulation of spiking neural networks on the multi-chip SpiNNaker system. The 2010 International Joint Conference on Neural Networks (IJCNN). http://doi.org/10.1109/ijcnn.2010.5596759
Jin, X., Rast, A., Galluppi, F., Davies, S., & Furber, S. (2010). Implementing spike-timing-dependent plasticity on SpiNNaker neuromorphic hardware. The 2010 International Joint Conference on Neural Networks (IJCNN). http://doi.org/10.1109/ijcnn.2010.5596372
Theses / Dissertations
Davies, S. (2013). Learning in Spiking Neural Networks. (Doctoral thesis).
I generally supervise projects in the area of neural networks, computer vision, embedded systems and parallel computing.