Generalized leaky integrate-and-fire models classify multiple neuron types

Corinne Teeter,Vilas Menon,Aaron Szafer,Jim Berg,Ramakrishnan Iyer,Stefan Mihalas,Christof Koch,Nicholas Cain,David Feng,Nathan Gouwens,Michael Hawrylycz,Hongkui Zeng

doi:10.1038/s41467-017-02717-4

Abstract

There is a high diversity of neuronal types in the mammalian neocortex. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a set of generalized leaky integrate-and-fire (GLIF) models of increasing complexity to reproduce the spiking behaviors of 645 recorded neurons from 16 transgenic lines. The more complex models have an increased capacity to predict spiking behavior of hold-out stimuli. We use unsupervised methods to classify cell types, and find that high level GLIF model parameters are able to differentiate transgenic lines comparable to electrophysiological features. The more complex model parameters also have an increased ability to differentiate between transgenic lines. Thus, creating simple models is an effective dimensionality reduction technique that enables the differentiation of cell types from electrophysiological responses without the need for a priori-defined features. This database will provide a set of simplified models of multiple cell types for the community to use in network models.

Highlights

There is a high diversity of neuronal types in the mammalian neocortex
We provide a sizable database of generalized leaky integrate-and-fire (GLIF) models that can facilitate the development of system models with cell types available in the Allen Cell Types Database at http://celltypes.brain-map. org/
We show that GLIF models can simultaneously reproduce the spike times of biological neurons and reduce the complex mapping between the input current to the spike-train output with a small number of parameters

Summary

Introduction

There is a high diversity of neuronal types in the mammalian neocortex. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. The classification of neurons into cell types is a conceptual simplification intended to reduce this complexity To this end, a large-scale effort at the Allen Institute for Brain Science has focused on characterizing the diversity of cell types in the primary visual cortex of the adult mouse using electrophysiology, morphological reconstructions, connectivity, and modeling in one standardized effort. One simplification that significantly reduces model complexity is to represent the entire dendritic tree, soma, and axonal hillock by a single compartment, while maintaining the dynamics of the individual conductances[3] This approximation is especially warranted when characterizing neurons via somatic current injection and voltage recording as is done in the Allen Cell Types Database. This spike-train focus allows us to generate models which are much simpler than biophysically detailed models but still capture a substantial amount of their complexity

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