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Stochastically gating ion channels enable patterned spike firing through activity-dependent modulation of spike probability.

Dudman JT, Nolan MF - PLoS Comput. Biol. (2009)

Bottom Line: Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization.Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization.Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.

View Article: PubMed Central - PubMed

Affiliation: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America. dudmanj@janelia.hhmi.org

ABSTRACT
The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.

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Ih during AHP recovery enhances spike probability andclustering.(A) Probability density plots for the magnitude of the net ionic currentwithin the voltage range −49.5 to −50.5 mV takenfrom epochs in which no action potentials occurred(“Silent”; black), preceding the initial spike of acluster (“Initial Spike”; red), or, during recoveryfrom the spike AHP (“AHP”; blue). Each plot is fitwith a Gaussian function, which was used to estimate the standarddeviation of the distribution. Areas were normalized toP = 1 and all distributions had nearlyidentical properties(σsteady-state = 16.6pA, σAHP = 16.6 pA,σsilence = 16.5pA). (B–C) Probability density plot for Ih (B) andfor INaP (C) during the same simulation epoch as in A. (D)Color-coded plot of the average membrane potential for all actionpotentials. Transition from red to blue color applies to E and F. Solidlines are derived from fits of Gaussian functions. (E) Phase plot of themean Ih during the spike. (F) Phase plot of the meanINaP during the spike. (E–F) Insets focus in onthe region of membrane potential selected for the plots inA–C.
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pcbi-1000290-g007: Ih during AHP recovery enhances spike probability andclustering.(A) Probability density plots for the magnitude of the net ionic currentwithin the voltage range −49.5 to −50.5 mV takenfrom epochs in which no action potentials occurred(“Silent”; black), preceding the initial spike of acluster (“Initial Spike”; red), or, during recoveryfrom the spike AHP (“AHP”; blue). Each plot is fitwith a Gaussian function, which was used to estimate the standarddeviation of the distribution. Areas were normalized toP = 1 and all distributions had nearlyidentical properties(σsteady-state = 16.6pA, σAHP = 16.6 pA,σsilence = 16.5pA). (B–C) Probability density plot for Ih (B) andfor INaP (C) during the same simulation epoch as in A. (D)Color-coded plot of the average membrane potential for all actionpotentials. Transition from red to blue color applies to E and F. Solidlines are derived from fits of Gaussian functions. (E) Phase plot of themean Ih during the spike. (F) Phase plot of the meanINaP during the spike. (E–F) Insets focus in onthe region of membrane potential selected for the plots inA–C.

Mentions: Fourth, to generate activity patterns that take place over relatively long timescales, such as spike clusters, a deterministic model requires relatively slowchanges in the state of the model and at least one of the model parameters mustvary as a function of a spike's location within a cluster. By contrast,the probabilistic mechanism of spike clustering does not require slow changes inmodel parameters beyond the recovery period from the AHP (Figure 6). Consistent with this prediction wefind that the distribution of currents during AHP recovery is not differentbetween the first spike in a cluster and all other spikes regardless of theirposition (see below, Figures7 and S8).


Stochastically gating ion channels enable patterned spike firing through activity-dependent modulation of spike probability.

Dudman JT, Nolan MF - PLoS Comput. Biol. (2009)

Ih during AHP recovery enhances spike probability andclustering.(A) Probability density plots for the magnitude of the net ionic currentwithin the voltage range −49.5 to −50.5 mV takenfrom epochs in which no action potentials occurred(“Silent”; black), preceding the initial spike of acluster (“Initial Spike”; red), or, during recoveryfrom the spike AHP (“AHP”; blue). Each plot is fitwith a Gaussian function, which was used to estimate the standarddeviation of the distribution. Areas were normalized toP = 1 and all distributions had nearlyidentical properties(σsteady-state = 16.6pA, σAHP = 16.6 pA,σsilence = 16.5pA). (B–C) Probability density plot for Ih (B) andfor INaP (C) during the same simulation epoch as in A. (D)Color-coded plot of the average membrane potential for all actionpotentials. Transition from red to blue color applies to E and F. Solidlines are derived from fits of Gaussian functions. (E) Phase plot of themean Ih during the spike. (F) Phase plot of the meanINaP during the spike. (E–F) Insets focus in onthe region of membrane potential selected for the plots inA–C.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2631146&req=5

pcbi-1000290-g007: Ih during AHP recovery enhances spike probability andclustering.(A) Probability density plots for the magnitude of the net ionic currentwithin the voltage range −49.5 to −50.5 mV takenfrom epochs in which no action potentials occurred(“Silent”; black), preceding the initial spike of acluster (“Initial Spike”; red), or, during recoveryfrom the spike AHP (“AHP”; blue). Each plot is fitwith a Gaussian function, which was used to estimate the standarddeviation of the distribution. Areas were normalized toP = 1 and all distributions had nearlyidentical properties(σsteady-state = 16.6pA, σAHP = 16.6 pA,σsilence = 16.5pA). (B–C) Probability density plot for Ih (B) andfor INaP (C) during the same simulation epoch as in A. (D)Color-coded plot of the average membrane potential for all actionpotentials. Transition from red to blue color applies to E and F. Solidlines are derived from fits of Gaussian functions. (E) Phase plot of themean Ih during the spike. (F) Phase plot of the meanINaP during the spike. (E–F) Insets focus in onthe region of membrane potential selected for the plots inA–C.
Mentions: Fourth, to generate activity patterns that take place over relatively long timescales, such as spike clusters, a deterministic model requires relatively slowchanges in the state of the model and at least one of the model parameters mustvary as a function of a spike's location within a cluster. By contrast,the probabilistic mechanism of spike clustering does not require slow changes inmodel parameters beyond the recovery period from the AHP (Figure 6). Consistent with this prediction wefind that the distribution of currents during AHP recovery is not differentbetween the first spike in a cluster and all other spikes regardless of theirposition (see below, Figures7 and S8).

Bottom Line: Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization.Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization.Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.

View Article: PubMed Central - PubMed

Affiliation: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America. dudmanj@janelia.hhmi.org

ABSTRACT
The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.

Show MeSH
Related in: MedlinePlus