Innovative deep learning approaches enable quantitative analyses of synaptic imaging datasets (C10)

• Subject Area: Molecular Biology and Physiology of Neurons and Glial Cells
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 317475864

In the current funding period, which we joined in October 2023, we created SynapseNet, a tool that automatically segments synaptic vesicles and other structures in EM. In the next funding period, we will develop methods for a comprehensive extraction of morphological and molecular synaptic information to foster experimental and computational research. We will also develop an artificial intelligence (AI) approach to speed up the simulation of synaptic dynamics of the average excitatory synapse (AVE-SYN) undertaken by Z02 (Bonn/Tetzlaff).

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1286: Quantitative Synaptology
• Applicant Institution: Georg-August-Universität Göttingen
• Project Head: Professor Dr. Constantin Pape