Workshop at the 29th Annual Computational Neuroscience Meeting 2020
During the Annual Computational Neuroscience Meeting 2020 – ECN fellow Alexandra Tzilivaki organizes a workshop entitled: Dissecting the role of interneurons in mnemonic functions using computational modelling approaches."
Many international renowned neuroscientists are invited to the workshop. Among other speakers, Einstein Visiting Fellow Prof. Roger Traub is also a lecturer!
Date: July 21st, 5-9 pm (CEST Berlin time)
1) Prof. Roger D Traub (IBM Research Center USA)
2) Prof. Frances Skinner (Krembil Research Institute, Canada)
3) Prof. Wilten Nicola (Hotchkiss Brain Institute, University of Calgary, Canada)
4) Prof. Tim Vogels (IST Austria, Oxford University UK)
5) Dr. Jiannis Taxidis (UCLA, USA)
CNS 2020 Crowdcast platform (link TBA)
Alexandra Tzilivaki (Chair)
Einstein Center of Neurosciences Berlin, Charité – Universitätsmedizin Berlin
Dr. Spiros Chavlis (co-organizer)
Please note that registration to the main meeting is free but required!
For info, please see: CNS website
For more information, please visit the workshop website https://spiroschv.github.io/ or email firstname.lastname@example.org / email@example.com
GABAergic interneurons comprise one of the main types of cells in the mammalian nervous system. They play a critical role in learning and memory processes via inhibition and disinhibition pathways. Interneurons exhibit a variety of structural, molecular, electrophysiological and connectivity features. This high degree of variability makes it quite challenging to delineate their role in mnemonic functions through current experimental approaches. Computational modeling approaches, on the other hand, are a prominent tool used to predict their contribution to acquisition, storage and retrieval of information. The aim of this workshop is to present the latest computational work that highlights the function of interneurons in learning and memory processes. Additionally, we will actively discuss the next steps on how modeling approaches, from single cell to network models level, would benefit future research on interneurons as pertaining to mnemonic functions.