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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 and                            clustering.(A) Probability density plots for the magnitude of the net ionic current                            within the voltage range −49.5 to −50.5 mV taken                            from epochs in which no action potentials occurred                            (“Silent”; black), preceding the initial spike of a                            cluster (“Initial Spike”; red), or, during recovery                            from the spike AHP (“AHP”; blue). Each plot is fit                            with a Gaussian function, which was used to estimate the standard                            deviation of the distribution. Areas were normalized to                            P = 1 and all distributions had nearly                            identical properties                            (σsteady-state = 16.6                            pA, σAHP = 16.6 pA,                                σsilence = 16.5                            pA). (B–C) Probability density plot for Ih (B) and                            for INaP (C) during the same simulation epoch as in A. (D)                            Color-coded plot of the average membrane potential for all action                            potentials. Transition from red to blue color applies to E and F. Solid                            lines are derived from fits of Gaussian functions. (E) Phase plot of the                            mean Ih during the spike. (F) Phase plot of the mean                                INaP during the spike. (E–F) Insets focus in on                            the region of membrane potential selected for the plots in                            A–C.
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pcbi-1000290-g007: Ih during AHP recovery enhances spike probability and clustering.(A) Probability density plots for the magnitude of the net ionic current within the voltage range −49.5 to −50.5 mV taken from epochs in which no action potentials occurred (“Silent”; black), preceding the initial spike of a cluster (“Initial Spike”; red), or, during recovery from the spike AHP (“AHP”; blue). Each plot is fit with a Gaussian function, which was used to estimate the standard deviation of the distribution. Areas were normalized to P = 1 and all distributions had nearly identical properties (σsteady-state = 16.6 pA, σAHP = 16.6 pA, σsilence = 16.5 pA). (B–C) Probability density plot for Ih (B) and for INaP (C) during the same simulation epoch as in A. (D) Color-coded plot of the average membrane potential for all action potentials. Transition from red to blue color applies to E and F. Solid lines are derived from fits of Gaussian functions. (E) Phase plot of the mean Ih during the spike. (F) Phase plot of the mean INaP during the spike. (E–F) Insets focus in on the region of membrane potential selected for the plots in A–C.

Mentions: Fourth, to generate activity patterns that take place over relatively long time scales, such as spike clusters, a deterministic model requires relatively slow changes in the state of the model and at least one of the model parameters must vary as a function of a spike's location within a cluster. By contrast, the probabilistic mechanism of spike clustering does not require slow changes in model parameters beyond the recovery period from the AHP (Figure 6). Consistent with this prediction we find that the distribution of currents during AHP recovery is not different between the first spike in a cluster and all other spikes regardless of their position (see below, Figures 7 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 and                            clustering.(A) Probability density plots for the magnitude of the net ionic current                            within the voltage range −49.5 to −50.5 mV taken                            from epochs in which no action potentials occurred                            (“Silent”; black), preceding the initial spike of a                            cluster (“Initial Spike”; red), or, during recovery                            from the spike AHP (“AHP”; blue). Each plot is fit                            with a Gaussian function, which was used to estimate the standard                            deviation of the distribution. Areas were normalized to                            P = 1 and all distributions had nearly                            identical properties                            (σsteady-state = 16.6                            pA, σAHP = 16.6 pA,                                σsilence = 16.5                            pA). (B–C) Probability density plot for Ih (B) and                            for INaP (C) during the same simulation epoch as in A. (D)                            Color-coded plot of the average membrane potential for all action                            potentials. Transition from red to blue color applies to E and F. Solid                            lines are derived from fits of Gaussian functions. (E) Phase plot of the                            mean Ih during the spike. (F) Phase plot of the mean                                INaP during the spike. (E–F) Insets focus in on                            the region of membrane potential selected for the plots in                            A–C.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2631146&req=5

pcbi-1000290-g007: Ih during AHP recovery enhances spike probability and clustering.(A) Probability density plots for the magnitude of the net ionic current within the voltage range −49.5 to −50.5 mV taken from epochs in which no action potentials occurred (“Silent”; black), preceding the initial spike of a cluster (“Initial Spike”; red), or, during recovery from the spike AHP (“AHP”; blue). Each plot is fit with a Gaussian function, which was used to estimate the standard deviation of the distribution. Areas were normalized to P = 1 and all distributions had nearly identical properties (σsteady-state = 16.6 pA, σAHP = 16.6 pA, σsilence = 16.5 pA). (B–C) Probability density plot for Ih (B) and for INaP (C) during the same simulation epoch as in A. (D) Color-coded plot of the average membrane potential for all action potentials. Transition from red to blue color applies to E and F. Solid lines are derived from fits of Gaussian functions. (E) Phase plot of the mean Ih during the spike. (F) Phase plot of the mean INaP during the spike. (E–F) Insets focus in on the region of membrane potential selected for the plots in A–C.
Mentions: Fourth, to generate activity patterns that take place over relatively long time scales, such as spike clusters, a deterministic model requires relatively slow changes in the state of the model and at least one of the model parameters must vary as a function of a spike's location within a cluster. By contrast, the probabilistic mechanism of spike clustering does not require slow changes in model parameters beyond the recovery period from the AHP (Figure 6). Consistent with this prediction we find that the distribution of currents during AHP recovery is not different between the first spike in a cluster and all other spikes regardless of their position (see below, Figures 7 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