Abstract
Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with or without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In previous work, the comprehensive knowledge base of hippocampal neuron types Hippocampome.org systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a complete set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. The models were created using an automated pipeline based on evolutionary algorithms. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the large-scale interactions of various neuron types at the mesoscopic level.
Highlights
Complex interactions among a myriad of neurons make it challenging to study the functions of brain regions
Detailed neuronal modeling frameworks often limit the scalability of such network simulations due to the specification of hundreds of equations governing each neuron’s intrinsic dynamics
We have accomplished a comprehensive mapping of experimentally identified intrinsic dynamics in a simple class of models with only two governing equations
Summary
Complex interactions among a myriad of neurons make it challenging to study the functions of brain regions. Each neuron is different, their landmark features such as the dendritic structure and patterns of somatic voltage spikes help define types of neurons, and such grouping allows for a tractable description and investigation of complex network interactions. Large-scale network models of brain regions can include precisely defined neuronal types to create a biologically realistic platform for hypothesis testing. A few studies have created large-scale network models of brain regions [2,3,4,5]. A large-scale description of thalamocortical systems [2], which used simplified phenomenological neuron models [6], simulated a network of a much larger scale (one million neurons and half a billion synapses), but it only included 22 abstract types among the neurons. Network modeling efforts more specific to the hippocampus include a full-scale model of the CA1 circuit [7] (~338,000 biophysically detailed neuron models of nine types), a large-scale model of the dentate gyrus [8] (~52,000 biophysically detailed neuron models of four types) and a large-scale model of CA1 [9] (~10,000 phenomenological models of two types)
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