On the Phase Reduction and Response Dynamics of Neural Oscillator Populations. Brown, E., Moehlis, J., & Holmes, P. Neural Computation, 16(4):673–715, April, 2004. ![On the Phase Reduction and Response Dynamics of Neural Oscillator Populations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex We undertake a probabilistic analysis of the response of repetitively firing neural populations to simple pulselike stimuli. Recalling and extending results from the literature, we compute phase response curves (PRCs) valid near bifurcations to periodic firing for Hindmarsh-Rose, Hodgkin-Huxley, Fitz Hugh-Nagumo, and Morris-Lecar models, encompassing the four generic (codimension one) bifurcations. Phase density equations are then used to analyze the role of the bifurcation, and the resulting PRC, in responses to stimuli. In particular, we explore the interplay among stimulus duration, baseline firing frequency, and population-level response patterns. We interpret the results in terms of the signal processing measure of gain and discuss further applications and experimentally testable predictions.
@article{brown2004,
title = {On the {Phase} {Reduction} and {Response} {Dynamics} of {Neural} {Oscillator} {Populations}},
volume = {16},
issn = {0899-7667, 1530-888X},
url = {https://direct.mit.edu/neco/article/16/4/673-715/6826},
doi = {10.1162/089976604322860668},
abstract = {We undertake a probabilistic analysis of the response of repetitively firing neural populations to simple pulselike stimuli. Recalling and extending results from the literature, we compute phase response curves (PRCs) valid near bifurcations to periodic firing for Hindmarsh-Rose, Hodgkin-Huxley, Fitz Hugh-Nagumo, and Morris-Lecar models, encompassing the four generic (codimension one) bifurcations. Phase density equations are then used to analyze the role of the bifurcation, and the resulting PRC, in responses to stimuli. In particular, we explore the interplay among stimulus duration, baseline firing frequency, and population-level response patterns. We interpret the results in terms of the signal processing measure of gain and discuss further applications and experimentally testable predictions.},
language = {en},
number = {4},
urldate = {2022-09-30},
journal = {Neural Computation},
author = {Brown, Eric and Moehlis, Jeff and Holmes, Philip},
month = apr,
year = {2004},
pages = {673--715},
}
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