Searching for optimal stimuli: Ascending a neuron's response function

Melinda Evrithiki Koelling; Duane Q. Nykamp

doi:10.1007/s10827-012-0395-7

Searching for optimal stimuli: Ascending a neuron's response function

Melinda Evrithiki Koelling, Duane Q. Nykamp

Mathematics

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. However, some neurons are strongly sensitive to multiple directions in stimulus space and have a highly nonlinear response. It can be difficult to find optimal stimuli for these neurons. We demonstrate how successive linear approximations of neuronal response can effectively carry out gradient ascent and move through stimulus space towards local maxima of the response. We demonstrate search results for a simple model neuron and two models of a highly selective neuron.

Original language	English (US)
Pages (from-to)	449-473
Number of pages	25
Journal	Journal of Computational Neuroscience
Volume	33
Issue number	3
DOIs	https://doi.org/10.1007/s10827-012-0395-7
State	Published - Dec 2012

Bibliographical note

Funding Information:
Acknowledgements We thank Teresa Nick for inspiring this line of research, Greg Horwitz for helpful discussions, and the anonymous reviewers for their suggestions. This research was supported by the National Science Foundation grant DMS-0719724 (DQN).

Keywords

Closed loop experiment
Gradient ascent
Mean firing rate maximization
Reverse correlation
Spike-triggered average

Access

10.1007/s10827-012-0395-7

Cite this

@article{1652d129de4941d2bd6561f72f1144d9,

title = "Searching for optimal stimuli: Ascending a neuron's response function",

abstract = "Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. However, some neurons are strongly sensitive to multiple directions in stimulus space and have a highly nonlinear response. It can be difficult to find optimal stimuli for these neurons. We demonstrate how successive linear approximations of neuronal response can effectively carry out gradient ascent and move through stimulus space towards local maxima of the response. We demonstrate search results for a simple model neuron and two models of a highly selective neuron.",

keywords = "Closed loop experiment, Gradient ascent, Mean firing rate maximization, Reverse correlation, Spike-triggered average",

author = "Koelling, {Melinda Evrithiki} and Nykamp, {Duane Q.}",

note = "Funding Information: Acknowledgements We thank Teresa Nick for inspiring this line of research, Greg Horwitz for helpful discussions, and the anonymous reviewers for their suggestions. This research was supported by the National Science Foundation grant DMS-0719724 (DQN).",

year = "2012",

month = dec,

doi = "10.1007/s10827-012-0395-7",

language = "English (US)",

volume = "33",

pages = "449--473",

journal = "Journal of Computational Neuroscience",

issn = "0929-5313",

publisher = "Springer Netherlands",

number = "3",

}

TY - JOUR

T1 - Searching for optimal stimuli

T2 - Ascending a neuron's response function

AU - Koelling, Melinda Evrithiki

AU - Nykamp, Duane Q.

N1 - Funding Information: Acknowledgements We thank Teresa Nick for inspiring this line of research, Greg Horwitz for helpful discussions, and the anonymous reviewers for their suggestions. This research was supported by the National Science Foundation grant DMS-0719724 (DQN).

PY - 2012/12

Y1 - 2012/12

N2 - Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. However, some neurons are strongly sensitive to multiple directions in stimulus space and have a highly nonlinear response. It can be difficult to find optimal stimuli for these neurons. We demonstrate how successive linear approximations of neuronal response can effectively carry out gradient ascent and move through stimulus space towards local maxima of the response. We demonstrate search results for a simple model neuron and two models of a highly selective neuron.

AB - Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. However, some neurons are strongly sensitive to multiple directions in stimulus space and have a highly nonlinear response. It can be difficult to find optimal stimuli for these neurons. We demonstrate how successive linear approximations of neuronal response can effectively carry out gradient ascent and move through stimulus space towards local maxima of the response. We demonstrate search results for a simple model neuron and two models of a highly selective neuron.

KW - Closed loop experiment

KW - Gradient ascent

KW - Mean firing rate maximization

KW - Reverse correlation

KW - Spike-triggered average

UR - http://www.scopus.com/inward/record.url?scp=84874108909&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84874108909&partnerID=8YFLogxK

U2 - 10.1007/s10827-012-0395-7

DO - 10.1007/s10827-012-0395-7

M3 - Article

C2 - 22580579

AN - SCOPUS:84874108909

VL - 33

SP - 449

EP - 473

JO - Journal of Computational Neuroscience

JF - Journal of Computational Neuroscience

SN - 0929-5313

IS - 3

ER -

Searching for optimal stimuli: Ascending a neuron's response function

Abstract

Bibliographical note

Keywords

Access

Other files and links

Fingerprint

Cite this