Limits...
Balanced plasticity and stability of the electrical properties of a molluscan modulatory interneuron after classical conditioning: a computational study.

Vavoulis DV, Nikitin ES, Kemenes I, Marra V, Feng J, Benjamin PR, Kemenes G - Front Behav Neurosci (2010)

Bottom Line: In order to understand the ionic mechanisms of this novel combination of plasticity and stability of intrinsic electrical properties, we first constructed and validated a Hodgkin-Huxley-type model of the CGCs.Including in the model an additional increase in the conductance of a high-voltage-activated calcium current allowed the spike amplitude and spike duration also to be maintained after conditioning.We conclude therefore that a balanced increase in three identified conductances is sufficient to explain the electrophysiological changes found in the CGCs after classical conditioning.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Warwick Coventry, UK.

ABSTRACT
The Cerebral Giant Cells (CGCs) are a pair of identified modulatory interneurons in the Central Nervous System of the pond snail Lymnaea stagnalis with an important role in the expression of both unconditioned and conditioned feeding behavior. Following single-trial food-reward classical conditioning, the membrane potential of the CGCs becomes persistently depolarized. This depolarization contributes to the conditioned response by facilitating sensory cell to command neuron synapses, which results in the activation of the feeding network by the conditioned stimulus. Despite the depolarization of the membrane potential, which enables the CGGs to play a key role in learning-induced network plasticity, there is no persistent change in the tonic firing rate or shape of the action potentials, allowing these neurons to retain their normal network function in feeding. In order to understand the ionic mechanisms of this novel combination of plasticity and stability of intrinsic electrical properties, we first constructed and validated a Hodgkin-Huxley-type model of the CGCs. We then used this model to elucidate how learning-induced changes in a somal persistent sodium and a delayed rectifier potassium current lead to a persistent depolarization of the CGCs whilst maintaining their firing rate. Including in the model an additional increase in the conductance of a high-voltage-activated calcium current allowed the spike amplitude and spike duration also to be maintained after conditioning. We conclude therefore that a balanced increase in three identified conductances is sufficient to explain the electrophysiological changes found in the CGCs after classical conditioning.

No MeSH data available.


Related in: MedlinePlus

Tolerance of optimal fitting to variation in parameter values. (A) Optimization against current-clamp data was repeated from a large number of randomly selected initial values for the maximal conductances and reversal potentials in the model, while the rest of the parameters were kept fixed to their previously estimated values. A total of 90 different initial values for each free parameter were tested, all of which converged to the same equally good fit to the data at the end of the optimization (Ai). Similarly, the optimization based on current-clamp data was repeated for parameters  and  (x = m,h,n) and  and δx (x = h,r,n,e,f), while the rest of the parameters in the model, including maximal conductances and reversal potentials, were kept fixed to their previously estimated values. We tested 140 randomly distributed initial values for each of the parameters  (x = m,h,n) and (x = h,r,n,e,f), among which only 63 (45%) converged to a sufficiently good fit to the current-clamp data (Aii, dashed rectangular region). All 230 points illustrated in Ai and Aii were normalized by dividing with the largest sum of squared residuals found in these simulations. (B) Examination of the estimated maximal conductance and reversal potential values corresponding to the optimal points illustrated in Ai revealed that these values were tightly constrained around the median, not exceeding 15% of this value (Bi). Similarly, the optimal values for parameters , , ,  and  (i.e. those corresponding to the points inside the rectangular dashed region in Aii) were tightly constrained around the median with the exception of the values for (a parameter controlling the steady-state inactivation of INaT; see Eq. S9 in section Materials and Methods in Supplementary Material), which were dispersed within 80% of the median value (Bii). On the other hand, the optimal values for most of the examined parameters  and δx (x = h,r,n,e,f) were rather broadly distributed around the median (Biii). All parameters in Bi–iii were centered and normalized with respect to the median. The lower and upper edges of each box in the same plots correspond to the 25th and 75th percentiles, respectively, while the whiskers above and below each box indicate the most extreme parameter values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2871690&req=5

Figure 3: Tolerance of optimal fitting to variation in parameter values. (A) Optimization against current-clamp data was repeated from a large number of randomly selected initial values for the maximal conductances and reversal potentials in the model, while the rest of the parameters were kept fixed to their previously estimated values. A total of 90 different initial values for each free parameter were tested, all of which converged to the same equally good fit to the data at the end of the optimization (Ai). Similarly, the optimization based on current-clamp data was repeated for parameters and (x = m,h,n) and and δx (x = h,r,n,e,f), while the rest of the parameters in the model, including maximal conductances and reversal potentials, were kept fixed to their previously estimated values. We tested 140 randomly distributed initial values for each of the parameters (x = m,h,n) and (x = h,r,n,e,f), among which only 63 (45%) converged to a sufficiently good fit to the current-clamp data (Aii, dashed rectangular region). All 230 points illustrated in Ai and Aii were normalized by dividing with the largest sum of squared residuals found in these simulations. (B) Examination of the estimated maximal conductance and reversal potential values corresponding to the optimal points illustrated in Ai revealed that these values were tightly constrained around the median, not exceeding 15% of this value (Bi). Similarly, the optimal values for parameters , , , and (i.e. those corresponding to the points inside the rectangular dashed region in Aii) were tightly constrained around the median with the exception of the values for (a parameter controlling the steady-state inactivation of INaT; see Eq. S9 in section Materials and Methods in Supplementary Material), which were dispersed within 80% of the median value (Bii). On the other hand, the optimal values for most of the examined parameters and δx (x = h,r,n,e,f) were rather broadly distributed around the median (Biii). All parameters in Bi–iii were centered and normalized with respect to the median. The lower and upper edges of each box in the same plots correspond to the 25th and 75th percentiles, respectively, while the whiskers above and below each box indicate the most extreme parameter values.

Mentions: The model includes all the previously identified voltage-gated ionic currents in the biological CGCs. For each current, the maximal conductance, reversal potential and activation/inactivation kinetics were estimated from existing voltage- and current-clamp electrophysiological data (Staras et al., 2002; Nikitin et al., 2006). In a first stage, the estimation of 18 of the 43 parameters governing the activation and inactivation kinetics of most currents in the model was possible from voltage-clamp data (Table 1). In a second stage, the remaining parameters, including the maximal conductance and reversal potential for each current, were estimated by fitting the whole-cell model against current-clamp data, while most of the parameters estimated from voltage-clamp data in the previous step were kept fixed, as explained below. The final values of all parameters in the model are given in Table 1 along with information on whether their estimation was based on voltage- or current-clamp data. Information on the variability of parameter values is given in Section “Tolerance of optimal fitting to variation in parameter values” and Figure 3.


Balanced plasticity and stability of the electrical properties of a molluscan modulatory interneuron after classical conditioning: a computational study.

Vavoulis DV, Nikitin ES, Kemenes I, Marra V, Feng J, Benjamin PR, Kemenes G - Front Behav Neurosci (2010)

Tolerance of optimal fitting to variation in parameter values. (A) Optimization against current-clamp data was repeated from a large number of randomly selected initial values for the maximal conductances and reversal potentials in the model, while the rest of the parameters were kept fixed to their previously estimated values. A total of 90 different initial values for each free parameter were tested, all of which converged to the same equally good fit to the data at the end of the optimization (Ai). Similarly, the optimization based on current-clamp data was repeated for parameters  and  (x = m,h,n) and  and δx (x = h,r,n,e,f), while the rest of the parameters in the model, including maximal conductances and reversal potentials, were kept fixed to their previously estimated values. We tested 140 randomly distributed initial values for each of the parameters  (x = m,h,n) and (x = h,r,n,e,f), among which only 63 (45%) converged to a sufficiently good fit to the current-clamp data (Aii, dashed rectangular region). All 230 points illustrated in Ai and Aii were normalized by dividing with the largest sum of squared residuals found in these simulations. (B) Examination of the estimated maximal conductance and reversal potential values corresponding to the optimal points illustrated in Ai revealed that these values were tightly constrained around the median, not exceeding 15% of this value (Bi). Similarly, the optimal values for parameters , , ,  and  (i.e. those corresponding to the points inside the rectangular dashed region in Aii) were tightly constrained around the median with the exception of the values for (a parameter controlling the steady-state inactivation of INaT; see Eq. S9 in section Materials and Methods in Supplementary Material), which were dispersed within 80% of the median value (Bii). On the other hand, the optimal values for most of the examined parameters  and δx (x = h,r,n,e,f) were rather broadly distributed around the median (Biii). All parameters in Bi–iii were centered and normalized with respect to the median. The lower and upper edges of each box in the same plots correspond to the 25th and 75th percentiles, respectively, while the whiskers above and below each box indicate the most extreme parameter values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2871690&req=5

Figure 3: Tolerance of optimal fitting to variation in parameter values. (A) Optimization against current-clamp data was repeated from a large number of randomly selected initial values for the maximal conductances and reversal potentials in the model, while the rest of the parameters were kept fixed to their previously estimated values. A total of 90 different initial values for each free parameter were tested, all of which converged to the same equally good fit to the data at the end of the optimization (Ai). Similarly, the optimization based on current-clamp data was repeated for parameters and (x = m,h,n) and and δx (x = h,r,n,e,f), while the rest of the parameters in the model, including maximal conductances and reversal potentials, were kept fixed to their previously estimated values. We tested 140 randomly distributed initial values for each of the parameters (x = m,h,n) and (x = h,r,n,e,f), among which only 63 (45%) converged to a sufficiently good fit to the current-clamp data (Aii, dashed rectangular region). All 230 points illustrated in Ai and Aii were normalized by dividing with the largest sum of squared residuals found in these simulations. (B) Examination of the estimated maximal conductance and reversal potential values corresponding to the optimal points illustrated in Ai revealed that these values were tightly constrained around the median, not exceeding 15% of this value (Bi). Similarly, the optimal values for parameters , , , and (i.e. those corresponding to the points inside the rectangular dashed region in Aii) were tightly constrained around the median with the exception of the values for (a parameter controlling the steady-state inactivation of INaT; see Eq. S9 in section Materials and Methods in Supplementary Material), which were dispersed within 80% of the median value (Bii). On the other hand, the optimal values for most of the examined parameters and δx (x = h,r,n,e,f) were rather broadly distributed around the median (Biii). All parameters in Bi–iii were centered and normalized with respect to the median. The lower and upper edges of each box in the same plots correspond to the 25th and 75th percentiles, respectively, while the whiskers above and below each box indicate the most extreme parameter values.
Mentions: The model includes all the previously identified voltage-gated ionic currents in the biological CGCs. For each current, the maximal conductance, reversal potential and activation/inactivation kinetics were estimated from existing voltage- and current-clamp electrophysiological data (Staras et al., 2002; Nikitin et al., 2006). In a first stage, the estimation of 18 of the 43 parameters governing the activation and inactivation kinetics of most currents in the model was possible from voltage-clamp data (Table 1). In a second stage, the remaining parameters, including the maximal conductance and reversal potential for each current, were estimated by fitting the whole-cell model against current-clamp data, while most of the parameters estimated from voltage-clamp data in the previous step were kept fixed, as explained below. The final values of all parameters in the model are given in Table 1 along with information on whether their estimation was based on voltage- or current-clamp data. Information on the variability of parameter values is given in Section “Tolerance of optimal fitting to variation in parameter values” and Figure 3.

Bottom Line: In order to understand the ionic mechanisms of this novel combination of plasticity and stability of intrinsic electrical properties, we first constructed and validated a Hodgkin-Huxley-type model of the CGCs.Including in the model an additional increase in the conductance of a high-voltage-activated calcium current allowed the spike amplitude and spike duration also to be maintained after conditioning.We conclude therefore that a balanced increase in three identified conductances is sufficient to explain the electrophysiological changes found in the CGCs after classical conditioning.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Warwick Coventry, UK.

ABSTRACT
The Cerebral Giant Cells (CGCs) are a pair of identified modulatory interneurons in the Central Nervous System of the pond snail Lymnaea stagnalis with an important role in the expression of both unconditioned and conditioned feeding behavior. Following single-trial food-reward classical conditioning, the membrane potential of the CGCs becomes persistently depolarized. This depolarization contributes to the conditioned response by facilitating sensory cell to command neuron synapses, which results in the activation of the feeding network by the conditioned stimulus. Despite the depolarization of the membrane potential, which enables the CGGs to play a key role in learning-induced network plasticity, there is no persistent change in the tonic firing rate or shape of the action potentials, allowing these neurons to retain their normal network function in feeding. In order to understand the ionic mechanisms of this novel combination of plasticity and stability of intrinsic electrical properties, we first constructed and validated a Hodgkin-Huxley-type model of the CGCs. We then used this model to elucidate how learning-induced changes in a somal persistent sodium and a delayed rectifier potassium current lead to a persistent depolarization of the CGCs whilst maintaining their firing rate. Including in the model an additional increase in the conductance of a high-voltage-activated calcium current allowed the spike amplitude and spike duration also to be maintained after conditioning. We conclude therefore that a balanced increase in three identified conductances is sufficient to explain the electrophysiological changes found in the CGCs after classical conditioning.

No MeSH data available.


Related in: MedlinePlus