Panaytora Poirazi is the Research Director and leader of the Instute of Molecular Biology and Biotechnology at the Foundation of Research and Technology. She graduated from the Mathematics Department of the University of Cyprus and then earned her MSc (1998) and her PhD (2000) in Biomedical Engineering from the University of Southern California.
Her research interests focus on understanding how dendrites contribute to complex brain functions, such as learning and memory. The dendrites, the thin branches that extend from the cell bodies of neurons, are equipped with non-linear mechanisms that give neurons improved capabilities for processing, learning and storing information. The laboratory of Dr. Poirazi develops and uses computer models that simulate dendritic calculations and their effects on cell function in different areas of the brain. In close collaboration with experimental laboratories in Germany and the United States, the laboratory also uses in vivo behavioral and imaging techniques to investigate the role of dendrites in mouse behavior. Finally, her team is developing brain-inspired algorithms that incorporate dendritic properties, with the goal of evolving machine learning tools and artificial intelligence.
Dr. Poirazi has received many prizes for her scientific work, such as the EMBO Young Researcher Award (2005), the highly competitive Young Researcher Award of the European Research Council (ERC Starting Grant, 2012), the internationally renowned Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt German Foundation (2018), the EINSTEIN visiting fellow (2019) award of the German EINSTEIN Foundation, Berlin and many more.
How dendrites help solve biological and machine learning problems
Dendrites are thin processes that extend from the cell body of neurons, the main computing units of the brain. The role of dendrites in complex brain functions has been investigated for several decades, yet their direct involvement in key behaviors such as for example sensory perception has only recently been established. In my presentation I will discuss how computational modelling has helped us illuminate dendritic function . I will present the main findings of a number of projects in lab dealing with dendritic nonlinearities in excitatory and inhibitory neurons and their consequences on memory formation , the role of dendrites in solving nonlinear problems in human neurons  and recent efforts to (i) advance machine learning algorithms by adopting dendritic features and (ii) build smart, hybrid systems for drug discovery.
 Panayiota Poirazi & Athanasia Papoutsi. Illuminating dendritic function with computational models. Nature Reviews Neuroscience, 11 May 2020 | DOI: 10.1038/s41583- 020-0301-7
 Tzilivaki A, Kastellakis G, Poirazi P. Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators. Nat Commun. 2019 Aug 14;10(1):3664. doi: 10.1038/s41467- 019-11537-7.
 Gidon A, Zolnik TA, Fidzinski P, Bolduan F, Papoutsi A, Poirazi P, Holtkamp M, Vida I, Larkum ME. Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science. 2020 Jan 3;367(6473):83-87. doi: 10.1126/science.aax6239.