Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics

The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons. Two main modes of single neuron dynamics–integration and resonance–have been distinguished. While resonator cell types exist in a variety of brain areas, few models incorporat...

Full description

Bibliographic Details
Main Authors:	Puelma Touzel, Maximilian, Wolf, Fred
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4697854/

id	pubmed-4697854
recordtype	oai_dc
spelling	pubmed-46978542016-01-13 Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics Puelma Touzel, Maximilian Wolf, Fred Research Article The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons. Two main modes of single neuron dynamics–integration and resonance–have been distinguished. While resonator cell types exist in a variety of brain areas, few models incorporate this feature and fewer have investigated its effects. To understand better how a resonator’s frequency preference emerges from its intrinsic dynamics and contributes to its local area’s population firing rate dynamics, we analyze the dynamic gain of an analytically solvable two-degree of freedom neuron model. In the Fokker-Planck approach, the dynamic gain is intractable. The alternative Gauss-Rice approach lifts the resetting of the voltage after a spike. This allows us to derive a complete expression for the dynamic gain of a resonator neuron model in terms of a cascade of filters on the input. We find six distinct response types and use them to fully characterize the routes to resonance across all values of the relevant timescales. We find that resonance arises primarily due to slow adaptation with an intrinsic frequency acting to sharpen and adjust the location of the resonant peak. We determine the parameter regions for the existence of an intrinsic frequency and for subthreshold and spiking resonance, finding all possible intersections of the three. The expressions and analysis presented here provide an account of how intrinsic neuron dynamics shape dynamic population response properties and can facilitate the construction of an exact theory of correlations and stability of population activity in networks containing populations of resonator neurons. Public Library of Science 2015-12-31 /pmc/articles/PMC4697854/ /pubmed/26720924 http://dx.doi.org/10.1371/journal.pcbi.1004636 Text en © 2015 Puelma Touzel, Wolf http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Puelma Touzel, Maximilian Wolf, Fred
spellingShingle	Puelma Touzel, Maximilian Wolf, Fred Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics
author_facet	Puelma Touzel, Maximilian Wolf, Fred
author_sort	Puelma Touzel, Maximilian
title	Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics
title_short	Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics
title_full	Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics
title_fullStr	Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics
title_full_unstemmed	Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics
title_sort	complete firing-rate response of neurons with complex intrinsic dynamics
description	The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons. Two main modes of single neuron dynamics–integration and resonance–have been distinguished. While resonator cell types exist in a variety of brain areas, few models incorporate this feature and fewer have investigated its effects. To understand better how a resonator’s frequency preference emerges from its intrinsic dynamics and contributes to its local area’s population firing rate dynamics, we analyze the dynamic gain of an analytically solvable two-degree of freedom neuron model. In the Fokker-Planck approach, the dynamic gain is intractable. The alternative Gauss-Rice approach lifts the resetting of the voltage after a spike. This allows us to derive a complete expression for the dynamic gain of a resonator neuron model in terms of a cascade of filters on the input. We find six distinct response types and use them to fully characterize the routes to resonance across all values of the relevant timescales. We find that resonance arises primarily due to slow adaptation with an intrinsic frequency acting to sharpen and adjust the location of the resonant peak. We determine the parameter regions for the existence of an intrinsic frequency and for subthreshold and spiking resonance, finding all possible intersections of the three. The expressions and analysis presented here provide an account of how intrinsic neuron dynamics shape dynamic population response properties and can facilitate the construction of an exact theory of correlations and stability of population activity in networks containing populations of resonator neurons.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4697854/
_version_	1613518897815748608

Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics

Similar Items