Limits...
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.

Show MeSH

Related in: MedlinePlus

Effects of stochastic channel gating alter the response to stellate                            cells to naturalistic stimuli.(A,B) Plot of mean spike frequency (A) and coefficient of variation of                            the ISI distribution (B) as a function of the mean and standard                            deviation of band limited white noise inputs obtained from 5 s duration                            simulations (N = 384). (C) CV plotted                            as a function of mean firing frequency for the same data shown in A and                            B. The frequency and CV of several recordings (see [49]) from                            neurons in the superifical layers of the medial entorhinal cortex                                in vivo are plotted for comparison (red dots).                            These values from in vivo data were used to define a                            region of stimulus space selected for further analysis (red box). (D) A                            masked plot of stimulus space shows the simulations that resulted in                            values within the red box defined in C. Longer simulations (150 s) were                            run for the points indicated in red using both the deterministic and                            stochastic models. (E) The mean ISI probability density for experimental                            recordings plotted in C. Gray shaded region indictates the range of ISIs                            for spike clusters (see text). (F, G) ISI histograms obtained                            from simulations with the deterministic (“D”) and                            stochastic (“S”) versions of the model using input                            statistics at the extrema of the plot in D (indicated by                            “F” and “G”). (H) The difference                            in spike counts between the D and S simulations for the data plotted in                            F (blue) and G (red). The stochastic model shows a selective                            redistribution in the probability of spiking that produces an increase                            in the clustering interval (shaded region) and a decrease at longer ISI                            intervals. (I) ISI histograms obtained from simulations with                            deterministic (blue) and stochastic (black) versions of the model using                            input statistics that fluctuate randomly between the two states                            indicated by the double-headed arrow in D. (J) The difference in spike                            counts between the D and S simulations for the data plotted in I. (K)                            ISI histogram for response of the knockout model to the unscaled                            (“us”; blue) and the scaled                            (“s”; red) poisson stimuli (see text). Gray shaded region is the data from I replotted. (L) The                            difference in count between the “us” and                            “s” simulations for the data plotted in K. All                            histograms use exponentially spaced bins.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2631146&req=5

pcbi-1000290-g009: Effects of stochastic channel gating alter the response to stellate cells to naturalistic stimuli.(A,B) Plot of mean spike frequency (A) and coefficient of variation of the ISI distribution (B) as a function of the mean and standard deviation of band limited white noise inputs obtained from 5 s duration simulations (N = 384). (C) CV plotted as a function of mean firing frequency for the same data shown in A and B. The frequency and CV of several recordings (see [49]) from neurons in the superifical layers of the medial entorhinal cortex in vivo are plotted for comparison (red dots). These values from in vivo data were used to define a region of stimulus space selected for further analysis (red box). (D) A masked plot of stimulus space shows the simulations that resulted in values within the red box defined in C. Longer simulations (150 s) were run for the points indicated in red using both the deterministic and stochastic models. (E) The mean ISI probability density for experimental recordings plotted in C. Gray shaded region indictates the range of ISIs for spike clusters (see text). (F, G) ISI histograms obtained from simulations with the deterministic (“D”) and stochastic (“S”) versions of the model using input statistics at the extrema of the plot in D (indicated by “F” and “G”). (H) The difference in spike counts between the D and S simulations for the data plotted in F (blue) and G (red). The stochastic model shows a selective redistribution in the probability of spiking that produces an increase in the clustering interval (shaded region) and a decrease at longer ISI intervals. (I) ISI histograms obtained from simulations with deterministic (blue) and stochastic (black) versions of the model using input statistics that fluctuate randomly between the two states indicated by the double-headed arrow in D. (J) The difference in spike counts between the D and S simulations for the data plotted in I. (K) ISI histogram for response of the knockout model to the unscaled (“us”; blue) and the scaled (“s”; red) poisson stimuli (see text). Gray shaded region is the data from I replotted. (L) The difference in count between the “us” and “s” simulations for the data plotted in K. All histograms use exponentially spaced bins.

Mentions: Could the stochastic model that we outline here also explain aspects of the firing patterns of neurons in behaving animals? Consistent with this possibility, spike times obtained from in vivo single unit recordings [49] show elevations (made clear by exponential bin spacing [50]) in their ISI distribution at around 100 ms (Figure 9E). This ISI resembles the peak of P(st/st0) in simulations of our stochastic model, but unlike the responses of our model to constant current input, the in vivo spike trains contain a much broader overall distribution of ISIs. To provide a more realistic comparison between the model and in vivo data, we therefore carried out stimulations of the response of the model neuron to simulated synaptic drive.


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

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

Effects of stochastic channel gating alter the response to stellate                            cells to naturalistic stimuli.(A,B) Plot of mean spike frequency (A) and coefficient of variation of                            the ISI distribution (B) as a function of the mean and standard                            deviation of band limited white noise inputs obtained from 5 s duration                            simulations (N = 384). (C) CV plotted                            as a function of mean firing frequency for the same data shown in A and                            B. The frequency and CV of several recordings (see [49]) from                            neurons in the superifical layers of the medial entorhinal cortex                                in vivo are plotted for comparison (red dots).                            These values from in vivo data were used to define a                            region of stimulus space selected for further analysis (red box). (D) A                            masked plot of stimulus space shows the simulations that resulted in                            values within the red box defined in C. Longer simulations (150 s) were                            run for the points indicated in red using both the deterministic and                            stochastic models. (E) The mean ISI probability density for experimental                            recordings plotted in C. Gray shaded region indictates the range of ISIs                            for spike clusters (see text). (F, G) ISI histograms obtained                            from simulations with the deterministic (“D”) and                            stochastic (“S”) versions of the model using input                            statistics at the extrema of the plot in D (indicated by                            “F” and “G”). (H) The difference                            in spike counts between the D and S simulations for the data plotted in                            F (blue) and G (red). The stochastic model shows a selective                            redistribution in the probability of spiking that produces an increase                            in the clustering interval (shaded region) and a decrease at longer ISI                            intervals. (I) ISI histograms obtained from simulations with                            deterministic (blue) and stochastic (black) versions of the model using                            input statistics that fluctuate randomly between the two states                            indicated by the double-headed arrow in D. (J) The difference in spike                            counts between the D and S simulations for the data plotted in I. (K)                            ISI histogram for response of the knockout model to the unscaled                            (“us”; blue) and the scaled                            (“s”; red) poisson stimuli (see text). Gray shaded region is the data from I replotted. (L) The                            difference in count between the “us” and                            “s” simulations for the data plotted in K. All                            histograms use exponentially spaced bins.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2631146&req=5

pcbi-1000290-g009: Effects of stochastic channel gating alter the response to stellate cells to naturalistic stimuli.(A,B) Plot of mean spike frequency (A) and coefficient of variation of the ISI distribution (B) as a function of the mean and standard deviation of band limited white noise inputs obtained from 5 s duration simulations (N = 384). (C) CV plotted as a function of mean firing frequency for the same data shown in A and B. The frequency and CV of several recordings (see [49]) from neurons in the superifical layers of the medial entorhinal cortex in vivo are plotted for comparison (red dots). These values from in vivo data were used to define a region of stimulus space selected for further analysis (red box). (D) A masked plot of stimulus space shows the simulations that resulted in values within the red box defined in C. Longer simulations (150 s) were run for the points indicated in red using both the deterministic and stochastic models. (E) The mean ISI probability density for experimental recordings plotted in C. Gray shaded region indictates the range of ISIs for spike clusters (see text). (F, G) ISI histograms obtained from simulations with the deterministic (“D”) and stochastic (“S”) versions of the model using input statistics at the extrema of the plot in D (indicated by “F” and “G”). (H) The difference in spike counts between the D and S simulations for the data plotted in F (blue) and G (red). The stochastic model shows a selective redistribution in the probability of spiking that produces an increase in the clustering interval (shaded region) and a decrease at longer ISI intervals. (I) ISI histograms obtained from simulations with deterministic (blue) and stochastic (black) versions of the model using input statistics that fluctuate randomly between the two states indicated by the double-headed arrow in D. (J) The difference in spike counts between the D and S simulations for the data plotted in I. (K) ISI histogram for response of the knockout model to the unscaled (“us”; blue) and the scaled (“s”; red) poisson stimuli (see text). Gray shaded region is the data from I replotted. (L) The difference in count between the “us” and “s” simulations for the data plotted in K. All histograms use exponentially spaced bins.
Mentions: Could the stochastic model that we outline here also explain aspects of the firing patterns of neurons in behaving animals? Consistent with this possibility, spike times obtained from in vivo single unit recordings [49] show elevations (made clear by exponential bin spacing [50]) in their ISI distribution at around 100 ms (Figure 9E). This ISI resembles the peak of P(st/st0) in simulations of our stochastic model, but unlike the responses of our model to constant current input, the in vivo spike trains contain a much broader overall distribution of ISIs. To provide a more realistic comparison between the model and in vivo data, we therefore carried out stimulations of the response of the model neuron to simulated synaptic drive.

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