A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Issue 4 (31st August 2019)

• Record Type: Journal Article
• Authors: Moradi, Keivan; Ascoli, Giorgio A.
• Other Names: Hasselmo Michael E. guestEditor.; Newman Ehren L. guestEditor.
• Is Part Of: Hippocampus. Volume 30:Issue 4(2020)
• Page Start: 314
• Page End: 331
• Publication Date: 2019-08-31
• Subjects: circuit biophysics -- computational biology -- information storage and retrieval -- knowledge bases -- models -- neuron types -- synapses/physiology; Hippocampus (Brain) -- Periodicals; 612.825
• DOI: 10.1002/hipo.23148
• Languages: English
• ISSNs: 1050-9631

Abstract

Abstract: The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer‐reviewed publications. However, no human‐ or machine‐readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state‐of‐the‐art information technology, we have developed a cloud‐based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1, 200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open‐access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40, 000 synaptic data entities covering 88% of the 3, 120 potential connections in Hippocampome.org . Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large‐scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open‐source at Hippocampome.org in order to further research across disciplines.