I am Professor of Computational Neuroscience & Neural Engineering in the department of Computer Science, Faculty of Engineering, University of Sheffield. I serve as Academic Editor for the scientific journals Scientific Reports, PLOS ONE and PeerJ and I am one of the directors of the Organisation for Computational Neurosciences. I am Head of the Machine Learning group and I lead the Computational Neuroscience Laboratory. I also serve as Deputy Departmental Director of Research.
Prior to my Academic appointment in Sheffield, I was Scientific Collaborator in the groups of Prof. Wulfram Gerstner at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and Prof. Walter Senn at the University of Bern. I hold a PhD in Computer Science and Artificial Intelligence (University of Sussex), a Masters in Microelectronics (University of Athens) and a Bachelors degree (with distinction) in Informatics & Telecommunications (University of Athens). I am a Chartered Engineer, registered with the Engineering Council UK in membership of the Institution of Engineering and Technology.
As a Computational Scientist and Engineer with extensive cross disciplinary experience, I contribute to the greater understanding of the brain’s wiring diagram via the use and development of unsupervised and reinforcement learning models, and their application to relevant research areas such as Neuromorphic Engineering.
My expertise is best summarised under the term “synaptic plasticity", which describes the rules under which the connectivity of our brain is shaped. My approach is that simple models have the power to help us understand the mechanisms that shape the brain wiring. For instance, my collaborative work with EPFL published in Nature Neuroscience has shed light on a long debate regarding the communication code in the brain, and how this is reflected by the neural connectivity. More recently, my work jointly with the University of Antwerp aims to explain the formation of connectivity motifs. While not overlooking the power of other approaches, I chose to mainly contribute via the use of models that maintain a degree of biological realism (spiking neuron models) but are free from the complexity of biophysical details.
Berdan, R., Vasilaki, E. , Wei, S. L., Khiat, A., Indiveri, G., Serb, A., and Prodromakis, T. (2016), Emulating short-term synaptic dynamics with memristive devices. Scientific Reports, Nature Publishing Group, 6, 18639; doi:10.1038/srep18639
Gehring, T. V., Luksys, G., Sandi, C., and Vasilaki, E. (2015) Detailed classification of swimming paths in the Morris Water Maze: multiple strategies within one trial. Scientific Reports, Nature Publishing Group, 5, 14562; doi: 10.1038/srep1456.
Vasilaki, E. & Gugliano, M. (2014), Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-term Plastic Synapses, PLoS ONE, 9(1): e84626. doi:10.1371/journal.pone.0084626.
Clopath, C., Buesing, L., Vasilaki, E., and Gerstner, W. (2010), Connectivity reflects Coding: A Model of Voltage-based Spike-Timing-Dependant-Plasticity with Homeostasis. Nature Neuroscience
Vasilaki, E., Fremaux, N.,Urbanczik, R., Senn, W, and Gerstner, W. (2009), Spike-based reinforcement learning in continuous State and Action Space: when policy gradient methods fail. PLOS Computational Biology, Vol.5(12):e1000586 doi:10.1371/journal.pcbi.1000586.
Welcome Trust, ‘The cortical representation of low-probability stimuli and its neuromorphic implementation’, Fellow Dr Vanattou-Saïfoudine, collaboration with the institute for NeuroInformatics ETHZ/University of Zurich (Sep 2016 - Aug 2019). Supervisor.