Intrinsic Diversity in Hippocampal Neurons: Phenomenological and Integrative Descriptions of Quantitative Dynamics
Date
2020
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Abstract
Simulations of neural systems promise to offer powerful frameworks to formulate and test hypotheses about the physical interactions intrinsic to the brain. A comprehensive characterization of experimentally observed neuronal diversity using a modeling system is necessary to simulate biologically realistic brain networks at the cell type level. Biophysically detailed model descriptions typically limit the scalability of such network simulations as they specify hundreds of equations governing each neuron’s intrinsic dynamics. On the other hand, simple phenomenological models, which compactly describe the patterns of neuronal excitability through dynamical bifurcations, often lack experimentally identifiable parameters. This makes it challenging for such models to quantitatively account for the intrinsic diversity experimentally observed among the neurons. In this work, compact model descriptions that comprehensively capture the intrinsic dynamical diversity observed among the rodent hippocampal neurons are created. Both point-neuron and compact multi-compartment models are optimized using evolutionary algorithms. These optimized models reflect the intrinsic differences among hippocampal neuron types both qualitatively and quantitatively. In addition, this work describes the collective dynamics of an ensemble of bursting neurons based on their self-organizing properties. Measures are formulated to quantify the metastable nature of the neural ensembles. Such integrative descriptions of neuronal dynamics can complement their intrinsic descriptions to accurately simulate biological neural circuits.