A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation

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 http://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 http://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 http://hippocampome.org in order to further research across disciplines.

An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo

“…The previous version of Hippocampome.org included 122 hippocampal and entorhinal cortex cell types [ 22 ] classified according to the main neurotransmitter, axonal and dendritic patterns, synaptic specificity [ 44 ], molecular biomarkers [ 45 , 46 ], membrane biophysics, and firing patterns [ 47 ]. Moreover, this knowledge base also associates each identified neuron type with known information on potential connectivity [ 48 ] and synaptic signals [ 49 ]. While information about action-potential waveforms and neuronal burstiness may assist classification [ 50 – 52 ] and investigations of spike initiation mechanisms [ 53 ], the capability to disambiguate between cell types based on these features remained suboptimal [ 54 ].…”

Data‐driven integration of hippocampal CA1 synaptic physiology in silico

“…where PSP exp (mV) and PSP model (mV) are the experimental and modeled PSPs amplitudes respectively and df = j E rev − V SS j (mV) is the driving force. For all the experiments we aimed to reproduce, E rev = − 8.5 mV was calculated for excitatory connections, while E rev = − 73 mV for inhibitory connections (Moradi & Ascoli, 2020). All simulations were run using the NEURON simulator as a core engine (Hines & Carnevale, 1997) with the Blue Brain Project's collection of hoc and NMODL (Hines & Carnevale, 2000) templates for parallel execution on supercomputers (Hines, Eichner, & Schürmann, 2008;Hines, Markram, & Schürmann, 2008).…”

“…were directly obtained from the literature (see Supplementary Table S1 for AMPAR and GABAR rise and decay time constants, methods for NMDAR time constants, and Supplementary Table S2 for reversal potentials (Moradi & Ascoli, 2020)). In particular, for the τ decay ( Supplementary Table S1) with the exception of Maccaferri, Roberts, Szucs, Cottingham, and Somogyi (2000) who used either single or weighted biexponential fits, none of the other studies we considered explicitly reported how τ decay was extracted.…”

“…Despite the wealth of data, we lack an integrative framework to reconcile the diversity of synaptic physiology, and therefore, identify knowledge gaps. There have been several noteworthy attempts to integrate knowledge on the cellular and synaptic components of hippocampal CA1 microcircuitry, which have provided a solid foundation to bring together anatomical properties and kinetic parameters of cell-type-specific connections-including the number of synapses per connection, connection probabilities, neurotransmitter release probabilities, and amplitudes of synaptic responses (Bezaire & Soltesz, 2013;Moradi & Ascoli, 2020;Wheeler et al, 2015). As a complementary endeavor, we extended a previously developed framework to reconstruct neocortical microcircuitry at the cellular and synaptic levels of detail (Markram et al, 2015), by integrating disparate data on the physiology of short-term dynamics of depression and facilitation of cell-type-specific synaptic transmission in hippocampal CA1.…”

Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization

“…This resource defines 122 neuron types based on their main neurotransmitter (glutamate or GABA) and the spatial distributions of their axons and dendrites (Wheeler et al, 2015). Hippocampome.org further annotates every neuron type with its reported connectivity (Rees et al, 2016), electrophysiological (Komendantov et al, 2019), molecular (White et al, 2020), synaptic (Moradi et al, 2020), morphological (Tecuatl et al, 2021), and functional (Sanchez-Aguilera et al, 2021) properties, in all cases providing links to the underlying experimental evidence. The ultimate goal of Hippocampome.org is to create biologically plausible computational models of the hippocampus (Venkadesh et al, 2019).…”