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

Corinne Teeter (), Ramakrishnan Iyer, Vilas Menon, Nathan Gouwens, David Feng, Jim Berg, Aaron Szafer, Nicholas Cain, Hongkui Zeng, Michael Hawrylycz, Christof Koch and Stefan Mihalas ()
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Corinne Teeter: Allen Institute for Brain Science
Ramakrishnan Iyer: Allen Institute for Brain Science
Vilas Menon: Allen Institute for Brain Science
Nathan Gouwens: Allen Institute for Brain Science
David Feng: Allen Institute for Brain Science
Jim Berg: Allen Institute for Brain Science
Aaron Szafer: Allen Institute for Brain Science
Nicholas Cain: Allen Institute for Brain Science
Hongkui Zeng: Allen Institute for Brain Science
Michael Hawrylycz: Allen Institute for Brain Science
Christof Koch: Allen Institute for Brain Science
Stefan Mihalas: Allen Institute for Brain Science

Nature Communications, 2018, vol. 9, issue 1, 1-15

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-017-02717-4

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