Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex

Abstract Limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal identification have so far prevented the systematic characterization of synaptic signals for all connections of any neural system. Introducing a novel strategy to overcome these challenges, we report the first comprehensive synaptic quantification among all known neuron types of the hippocampal-entorhinal network. First, we reconstructed > 2,600 synaptic traces from ~ 1,200 publications into a unified model of synaptic dynamics. We then trained a deep learning architecture with the resulting parameters, each annotated with detailed metadata. The model learned to predict the synaptic properties of all 3,120 circuit connections in arbitrary conditions with accuracy approaching the intrinsic experimental variability. Analysis of normalized data revealed that synaptic signals are controlled by few latent variables associated with specific molecular markers and interrelating conductance, kinetics, and short-term plasticity. We freely release the tools and full dataset of unitary synaptic values in 32 covariate settings via Hippocampome.org.