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Systematic Data Mining of Hippocampal Synaptic Properties

  • Keivan Moradi
  • Giorgio A. AscoliEmail author
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI)

Abstract

Synaptic electrophysiology has been extensively investigated in the rodent hippocampal formation for several decades. The strength, duration, and plasticity of excitatory and inhibitory signals depend both on the presynaptic and postsynaptic neuron types and vary substantially among subregions (dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex) and layers (e.g., oriens and radiatum). While certain connections are better characterized (e.g., the Schaffer collateral from CA3 pyramidal to CA1 pyramidal cells), the lack of a systematic accounting of published synaptic data prevents a comprehensive comparison across the whole circuit. Hippocampome.org, a knowledge base that identified over 100 neuron types based on morphological, electrophysiological, and molecular evidence, enables integration and dense coverage of the available synaptic data. Peters’ Rule predicts more than 3000 “potential connections” among neuron types. Extensive literature mining revealed electrophysiological properties for approximately 50% of these potential synapses at neuron-type level in peer-reviewed publications. In these cases, we extract information about synaptic amplitude, kinetics, and, when available, short-term and long-term plasticity. Due to widely nonuniform experimental methods and conditions, these data must be normalized and modeled to enable meaningful quantifications. The resulting type-based organized and integrated data will facilitate large-scale data-driven simulations of the entire hippocampal formation.

Keywords

Neural networks Microcircuits Connectivity Neuroinformatics Physiology Neuron types IPSC IPSP EPSC EPSP 

Notes

Acknowledgments

This project is supported by grants R01NS39600 (NIH), MURI N00014–10–1-0198 (ONR), NAKFI (Keck), CENTEC (AFOSR), Robust Intelligence (NSF), and Northrop Grumman.

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA

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