2023-10-21, 14:00–14:30 (Europe/Athens), Α115-117
Dendrites, the intricate and branched structures of neurons, play a pivotal role in information processing within the brain. Understanding their behavior at various levels of abstraction is crucial for advancing our knowledge of neural function and the development of therapeutic interventions. In this presentation, we introduce a novel approach to simulating dendrites using open-source software, shedding light on their essential mechanisms and functions. Our research leverages a multi-level abstraction framework, allowing us to model dendritic structures at different scales, from biophysical details to high-level functional representations. By employing open-source software, we aim to democratize access to advanced dendritic modeling tools, fostering collaboration and innovation within the scientific community. We discuss the potential applications of our software in neuroscience research, neuroinformatics, and artificial intelligence, highlighting its relevance in advancing our understanding of neural computation and inspiring novel computational paradigms. Ultimately, our goal is to make advanced dendritic modeling accessible to a wider audience, catalyzing breakthroughs in neuroscience and related fields, and contributing to our understanding of the brain's intricate workings and the development of innovative technologies and therapies.
Spyridon Chavlis received his diploma degree (equivalent to M.Eng.) in Mechanical Engineering from the National Technical University of Athens, Athens, Greece, in 2011, his M.Sc. degree in Biomedical Engineering from Imperial College London, London, U.K., in 2013, and his Ph.D. in Biology from the University of Crete, Heraklion, Greece, in 2018. During his doctoral studies, he developed a computational model of the hippocampal dentate gyrus examining the role of dendrites in the pattern separation mnemonic process. He is a Postdoctoral Researcher in computational neuroscience with the Poirazi Lab, IMBB-FORTH. He is interested in unraveling the underlying mechanisms of memory formation and learning using computational models of the hippocampal subregions and incorporating them in bio-inspired deep learning architectures.