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 68 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects these variabilities and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified “synapse types,” i.e. 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 synapse types. With this system, we have collected nearly 39,000 synaptic data entities covering 88% of the 3121 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 dataset 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.

Data‐driven integration of hippocampal CA1 synaptic physiology in silico

“…The underlying synapse model consisted of several parts, each with their associated parameters, which we determined in a six‐step procedure: We modeled synaptic connections with biexponential conductances requiring 8 parameters. Three parameters ( E rev , τ rise , τ decay ) 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.…”

“…In the continuing spirit of bringing together, hippocampal synaptic electrophysiology from published literature a recent complementary study leveraged text‐mining techniques to extract the properties of synaptic connections in hippocampal CA1, including PSP amplitudes and peak conductances (Moradi & Ascoli, 2020). The authors have also open‐sourced their collection of papers and parameters alongside useful cloud‐based tools to calculate reversal potentials and LJPs, of which we took advantage for this paper.…”

“…Finally, the mean PSP amplitude was compared against experimentally data and the peak conductance value was calibrated using the formula: where PSP exp (mV) and PSP model (mV) are the experimental and modeled PSPs amplitudes respectively and df = ∣ E rev − V SS ∣ (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).…”

“…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.…”

Overview of computational models of hippocampus and related structures: Introduction to the special issue

“…The functional importance of synaptic physiology in the hippocampus, coupled with the ease of stimulating the anatomically segregated pathways in different layers of the hippocampus such as stratum radiatum, led to an explosion of data on the synaptic physiology of the hippocampus. A catalog of this massive database of synaptic physiology is described in the special issue article by Moradi and Ascoli (). This article provides a framework to allow computational modelers to access the broad database of physiological experiments and constrain the parameters of detailed biophysical models.…”