Patterns of interval correlations in neural oscillators with adaptation
Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbi...
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Frontiers Media S.A.
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3843362/ |
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pubmed-38433622013-12-13 Patterns of interval correlations in neural oscillators with adaptation Schwalger, Tilo Lindner, Benjamin Neuroscience Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbitrary strength and time scale. Our weak-noise theory provides a general relation between the correlations and the phase-response curve (PRC) of the oscillator, proves anti-correlations between neighboring intervals for adapting neurons with type I PRC and identifies a single order parameter that determines the qualitative pattern of correlations. Monotonically decaying or oscillating correlation structures can be related to qualitatively different voltage traces after spiking, which can be explained by the phase plane geometry. At high firing rates, the long-term variability of the spike train associated with the cumulative interval correlations becomes small, independent of model details. Our results are verified by comparison with stochastic simulations of the exponential, leaky, and generalized integrate-and-fire models with adaptation. Frontiers Media S.A. 2013-11-29 /pmc/articles/PMC3843362/ /pubmed/24348372 http://dx.doi.org/10.3389/fncom.2013.00164 Text en Copyright © 2013 Schwalger and Lindner. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| 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 |
Schwalger, Tilo Lindner, Benjamin |
| spellingShingle |
Schwalger, Tilo Lindner, Benjamin Patterns of interval correlations in neural oscillators with adaptation |
| author_facet |
Schwalger, Tilo Lindner, Benjamin |
| author_sort |
Schwalger, Tilo |
| title |
Patterns of interval correlations in neural oscillators with adaptation |
| title_short |
Patterns of interval correlations in neural oscillators with adaptation |
| title_full |
Patterns of interval correlations in neural oscillators with adaptation |
| title_fullStr |
Patterns of interval correlations in neural oscillators with adaptation |
| title_full_unstemmed |
Patterns of interval correlations in neural oscillators with adaptation |
| title_sort |
patterns of interval correlations in neural oscillators with adaptation |
| description |
Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbitrary strength and time scale. Our weak-noise theory provides a general relation between the correlations and the phase-response curve (PRC) of the oscillator, proves anti-correlations between neighboring intervals for adapting neurons with type I PRC and identifies a single order parameter that determines the qualitative pattern of correlations. Monotonically decaying or oscillating correlation structures can be related to qualitatively different voltage traces after spiking, which can be explained by the phase plane geometry. At high firing rates, the long-term variability of the spike train associated with the cumulative interval correlations becomes small, independent of model details. Our results are verified by comparison with stochastic simulations of the exponential, leaky, and generalized integrate-and-fire models with adaptation. |
| publisher |
Frontiers Media S.A. |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3843362/ |
| _version_ |
1612031646501961728 |