Biologically realistic computer simulations of neuronal circuits require systematic data-driven modeling of neuron type-specific synaptic activity. However, limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal identification have so far prevented the consistent characterization of synaptic signals for all connections of any neural system. We introduce a strategy to overcome these challenges and report a comprehensive synaptic quantification among all known neuron types of the hippocampal-entorhinal network. First, we reconstructed >2600 synaptic traces from ∼1200 publications into a unified computational representation of synaptic dynamics. We then trained a deep learning architecture with the resulting parameters, each annotated with detailed metadata such as recording method, solutions, and temperature. 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 data normalized and completed with the deep learning model revealed that synaptic signals are controlled by few latent variables associated with specific molecular markers and interrelating conductance, decay time constant, and short-term plasticity. We freely release the tools and full dataset of unitary synaptic values in 32 covariate settings. Normalized synaptic data can be used in brain simulations, and to predict and test experimental hypothesis.

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.