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Variability in State-Dependent Plasticity of Intrinsic Properties during Cell-Autonomous Self-Regulation of Calcium Homeostasis in Hippocampal Model Neurons(1,2,3).

Srikanth S, Narayanan R - eNeuro (2015)

Bottom Line: Although calcium homeostasis emerged efficaciously across all models in the population, disparate changes in ionic conductances that mediated this emergence resulted in variable plasticity to several intrinsic properties, also manifesting as significant differences in firing responses across models.We found that the conductance values, intrinsic properties, and firing response of neurons exhibited differential robustness to an intervening switch in the type of afferent activity.These results unveil critical dissociations between different forms of homeostasis, and call for a systematic evaluation of the impact of state-dependent switches in afferent activity on neuronal intrinsic properties during neural coding and homeostasis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science , Bangalore 560 012, India ; Undergraduate program, Indian Institute of Science , Bangalore 560 012, India.

ABSTRACT
How do neurons reconcile the maintenance of calcium homeostasis with perpetual switches in patterns of afferent activity? Here, we assessed state-dependent evolution of calcium homeostasis in a population of hippocampal pyramidal neuron models, through an adaptation of a recent study on stomatogastric ganglion neurons. Calcium homeostasis was set to emerge through cell-autonomous updates to 12 ionic conductances, responding to different types of synaptically driven afferent activity. We first assessed the impact of theta-frequency inputs on the evolution of ionic conductances toward maintenance of calcium homeostasis. Although calcium homeostasis emerged efficaciously across all models in the population, disparate changes in ionic conductances that mediated this emergence resulted in variable plasticity to several intrinsic properties, also manifesting as significant differences in firing responses across models. Assessing the sensitivity of this form of plasticity, we noted that intrinsic neuronal properties and the firing response were sensitive to the target calcium concentration and to the strength and frequency of afferent activity. Next, we studied the evolution of calcium homeostasis when afferent activity was switched, in different temporal sequences, between two behaviorally distinct types of activity: theta-frequency inputs and sharp-wave ripples riding on largely silent periods. We found that the conductance values, intrinsic properties, and firing response of neurons exhibited differential robustness to an intervening switch in the type of afferent activity. These results unveil critical dissociations between different forms of homeostasis, and call for a systematic evaluation of the impact of state-dependent switches in afferent activity on neuronal intrinsic properties during neural coding and homeostasis.

No MeSH data available.


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Weak correlations between underlying parameters in valid models that emerged from global sensitivity analysis spanning 48 parameters. A, Pairwise interactions of the 48 parameters of the valid model population consisting of 78 models. Blue scatter plots represent parametric pairs whose correlation coefficient was more than 0.4, whereas red scatter plots indicate pairs with correlation coefficient less than –0.4. Bottom, The normalized histograms of the 48 parameters across the 78 valid models. B, Color-coded plot denoting the correlation coefficients for the corresponding scatter plots in A. C, Histogram of the 1128 correlation coefficients corresponding to the scatter plots in A.
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Figure 2: Weak correlations between underlying parameters in valid models that emerged from global sensitivity analysis spanning 48 parameters. A, Pairwise interactions of the 48 parameters of the valid model population consisting of 78 models. Blue scatter plots represent parametric pairs whose correlation coefficient was more than 0.4, whereas red scatter plots indicate pairs with correlation coefficient less than –0.4. Bottom, The normalized histograms of the 48 parameters across the 78 valid models. B, Color-coded plot denoting the correlation coefficients for the corresponding scatter plots in A. C, Histogram of the 1128 correlation coefficients corresponding to the scatter plots in A.

Mentions: As a first step in assessing state-dependence of cell-autonomous calcium homeostasis in hippocampal neurons, we generated several biophysically realistic models of CA1 pyramidal neurons using the GSA approach. Specifically, we created a cylindrical base neuronal model (100 × 100 μm) containing 12 ion channels (Leak, NaF, KDR, KA, KM, HCN, CaL, CaT, CaN, CaR, BK, and SK) and AMPA and NMDA receptors (Fig. 1A). We hand-tuned the base model such that the seven intrinsic measurements namely, f250, VAP, Rin, /Z/max, fR, Q, and ΦL (Fig. 1B–H) were within experimentally determined ranges (Narayanan and Johnston, 2007, 2008; Narayanan et al., 2010). We then uniformly sampled 48 parameters (spanning passive properties and densities/kinetics of channels in the neuron) from a range determined from the corresponding base model values to generate 4000 neurons (Table 1). We obtained seven measurements from each of these 4000 model neurons, and compared the measurements against their experimental counterparts. A model neuron was declared valid if all seven measurements of the model fell within their respective experimental bounds (Table 2). Upon imposing these experimental constraints on measurements from the 4000 models, we found 78 (∼2%) models to be valid. To test whether there were correlations between channel expression profiles and their kinetics, we asked whether there were pairwise correlations between the values of the 48 parameters associated with these 78 valid models. Consistent with previous results on hippocampal neurons (Rathour and Narayanan, 2012a, 2014), we found the parametric values to be weakly correlated (Fig 2A) with the range of correlation coefficients ranging from –0.6 to 0.6 (Fig 2B–C). Importantly, of the 1128 correlation coefficients, 1125 were in the range of –0.4 to 0.4 suggesting weak pairwise relationships between parameters in the model. As the 78 models were valid models (referred to as GSA models in what follows) for hippocampal pyramidal neuron physiology, we used these for our analysis on state-dependence of intrinsic properties in regulating calcium homeostasis.


Variability in State-Dependent Plasticity of Intrinsic Properties during Cell-Autonomous Self-Regulation of Calcium Homeostasis in Hippocampal Model Neurons(1,2,3).

Srikanth S, Narayanan R - eNeuro (2015)

Weak correlations between underlying parameters in valid models that emerged from global sensitivity analysis spanning 48 parameters. A, Pairwise interactions of the 48 parameters of the valid model population consisting of 78 models. Blue scatter plots represent parametric pairs whose correlation coefficient was more than 0.4, whereas red scatter plots indicate pairs with correlation coefficient less than –0.4. Bottom, The normalized histograms of the 48 parameters across the 78 valid models. B, Color-coded plot denoting the correlation coefficients for the corresponding scatter plots in A. C, Histogram of the 1128 correlation coefficients corresponding to the scatter plots in A.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596012&req=5

Figure 2: Weak correlations between underlying parameters in valid models that emerged from global sensitivity analysis spanning 48 parameters. A, Pairwise interactions of the 48 parameters of the valid model population consisting of 78 models. Blue scatter plots represent parametric pairs whose correlation coefficient was more than 0.4, whereas red scatter plots indicate pairs with correlation coefficient less than –0.4. Bottom, The normalized histograms of the 48 parameters across the 78 valid models. B, Color-coded plot denoting the correlation coefficients for the corresponding scatter plots in A. C, Histogram of the 1128 correlation coefficients corresponding to the scatter plots in A.
Mentions: As a first step in assessing state-dependence of cell-autonomous calcium homeostasis in hippocampal neurons, we generated several biophysically realistic models of CA1 pyramidal neurons using the GSA approach. Specifically, we created a cylindrical base neuronal model (100 × 100 μm) containing 12 ion channels (Leak, NaF, KDR, KA, KM, HCN, CaL, CaT, CaN, CaR, BK, and SK) and AMPA and NMDA receptors (Fig. 1A). We hand-tuned the base model such that the seven intrinsic measurements namely, f250, VAP, Rin, /Z/max, fR, Q, and ΦL (Fig. 1B–H) were within experimentally determined ranges (Narayanan and Johnston, 2007, 2008; Narayanan et al., 2010). We then uniformly sampled 48 parameters (spanning passive properties and densities/kinetics of channels in the neuron) from a range determined from the corresponding base model values to generate 4000 neurons (Table 1). We obtained seven measurements from each of these 4000 model neurons, and compared the measurements against their experimental counterparts. A model neuron was declared valid if all seven measurements of the model fell within their respective experimental bounds (Table 2). Upon imposing these experimental constraints on measurements from the 4000 models, we found 78 (∼2%) models to be valid. To test whether there were correlations between channel expression profiles and their kinetics, we asked whether there were pairwise correlations between the values of the 48 parameters associated with these 78 valid models. Consistent with previous results on hippocampal neurons (Rathour and Narayanan, 2012a, 2014), we found the parametric values to be weakly correlated (Fig 2A) with the range of correlation coefficients ranging from –0.6 to 0.6 (Fig 2B–C). Importantly, of the 1128 correlation coefficients, 1125 were in the range of –0.4 to 0.4 suggesting weak pairwise relationships between parameters in the model. As the 78 models were valid models (referred to as GSA models in what follows) for hippocampal pyramidal neuron physiology, we used these for our analysis on state-dependence of intrinsic properties in regulating calcium homeostasis.

Bottom Line: Although calcium homeostasis emerged efficaciously across all models in the population, disparate changes in ionic conductances that mediated this emergence resulted in variable plasticity to several intrinsic properties, also manifesting as significant differences in firing responses across models.We found that the conductance values, intrinsic properties, and firing response of neurons exhibited differential robustness to an intervening switch in the type of afferent activity.These results unveil critical dissociations between different forms of homeostasis, and call for a systematic evaluation of the impact of state-dependent switches in afferent activity on neuronal intrinsic properties during neural coding and homeostasis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science , Bangalore 560 012, India ; Undergraduate program, Indian Institute of Science , Bangalore 560 012, India.

ABSTRACT
How do neurons reconcile the maintenance of calcium homeostasis with perpetual switches in patterns of afferent activity? Here, we assessed state-dependent evolution of calcium homeostasis in a population of hippocampal pyramidal neuron models, through an adaptation of a recent study on stomatogastric ganglion neurons. Calcium homeostasis was set to emerge through cell-autonomous updates to 12 ionic conductances, responding to different types of synaptically driven afferent activity. We first assessed the impact of theta-frequency inputs on the evolution of ionic conductances toward maintenance of calcium homeostasis. Although calcium homeostasis emerged efficaciously across all models in the population, disparate changes in ionic conductances that mediated this emergence resulted in variable plasticity to several intrinsic properties, also manifesting as significant differences in firing responses across models. Assessing the sensitivity of this form of plasticity, we noted that intrinsic neuronal properties and the firing response were sensitive to the target calcium concentration and to the strength and frequency of afferent activity. Next, we studied the evolution of calcium homeostasis when afferent activity was switched, in different temporal sequences, between two behaviorally distinct types of activity: theta-frequency inputs and sharp-wave ripples riding on largely silent periods. We found that the conductance values, intrinsic properties, and firing response of neurons exhibited differential robustness to an intervening switch in the type of afferent activity. These results unveil critical dissociations between different forms of homeostasis, and call for a systematic evaluation of the impact of state-dependent switches in afferent activity on neuronal intrinsic properties during neural coding and homeostasis.

No MeSH data available.


Related in: MedlinePlus