Kurzfassung

The connectome of the cerebral cortex is highly dynamic, exhibiting high turnover of synaptic connections even under basal conditions. Nevertheless, our brains are able to maintain life-long memories. How are such memories formed and maintained in such a dynamic environment? Here, we propose to combine time lapse imaging of excitatory and inhibitory synaptic connectivity of rodent cortex during and after learning with high-throughput automated data analysis and computational modeling to help...The connectome of the cerebral cortex is highly dynamic, exhibiting high turnover of synaptic connections even under basal conditions. Nevertheless, our brains are able to maintain life-long memories. How are such memories formed and maintained in such a dynamic environment? Here, we propose to combine time lapse imaging of excitatory and inhibitory synaptic connectivity of rodent cortex during and after learning with high-throughput automated data analysis and computational modeling to help answer this fundamental question. First, we will use time lapse imaging technology developed during the first round of funding to simultaneously measure the dynamics of excitatory and inhibitory connectivity in the auditory cortex of mice during learning of an auditory go/no-go task. Second, we will develop high throughput automated image analysis techniques based on deep neural networks to perform automated quantification of the dynamics of excitatory and inhibitory connections. Third, we will use computational modeling to describe and explain the observed connectome dynamics during learning to reveal possible underlying mechanisms and generate testable predictions for future experiments. » weiterlesen» einklappen