Projects

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 safeguarded in such a dynamic environment? Here, we propose to combine time lapse imaging of excitatory and inhibitory synaptic connectivity of rodent cortex during learning with high-throughput automated data analysis and computational modeling to help answer this fundamental question. Our collaborative effort during the first funding period has laid the crucial methodological and analytical foundation for this task: First, we will use time lapse imaging technology developed during the first period of funding to measure the dynamics of excitatory and inhibitory connectivity in the auditory cortex of mice before, during, and after learning of an auditory cued go/nogo task. Second, we will apply and further refine high throughput automated image analysis techniques based on deep neural networks developed during the first funding period 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. Combining the complementary expertise from three laboratories, we aim to extend current descriptions of learning-induced synaptic plasticity from the single-dendrite or single-neuron towards the connectome level.