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Phasic firing in vasopressin cells: understanding its functional significance through computational models.

MacGregor DJ, Leng G - PLoS Comput. Biol. (2012)

Bottom Line: By comparison with the non-phasic population, the phasic population responds linearly to increases in tonic synaptic input.Non-phasic cells respond to transient elevations in synaptic input in a way that strongly depends on background activity levels, phasic cells in a way that is independent of background levels, and show a similar strong linearization of the response.These findings show large differences in information coding between the populations, and apparent functional advantages of asynchronous phasic firing.

View Article: PubMed Central - PubMed

Affiliation: Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Vasopressin neurons, responding to input generated by osmotic pressure, use an intrinsic mechanism to shift from slow irregular firing to a distinct phasic pattern, consisting of long bursts and silences lasting tens of seconds. With increased input, bursts lengthen, eventually shifting to continuous firing. The phasic activity remains asynchronous across the cells and is not reflected in the population output signal. Here we have used a computational vasopressin neuron model to investigate the functional significance of the phasic firing pattern. We generated a concise model of the synaptic input driven spike firing mechanism that gives a close quantitative match to vasopressin neuron spike activity recorded in vivo, tested against endogenous activity and experimental interventions. The integrate-and-fire based model provides a simple physiological explanation of the phasic firing mechanism involving an activity-dependent slow depolarising afterpotential (DAP) generated by a calcium-inactivated potassium leak current. This is modulated by the slower, opposing, action of activity-dependent dendritic dynorphin release, which inactivates the DAP, the opposing effects generating successive periods of bursting and silence. Model cells are not spontaneously active, but fire when perturbed by random perturbations mimicking synaptic input. We constructed one population of such phasic neurons, and another population of similar cells but which lacked the ability to fire phasically. We then studied how these two populations differed in the way that they encoded changes in afferent inputs. By comparison with the non-phasic population, the phasic population responds linearly to increases in tonic synaptic input. Non-phasic cells respond to transient elevations in synaptic input in a way that strongly depends on background activity levels, phasic cells in a way that is independent of background levels, and show a similar strong linearization of the response. These findings show large differences in information coding between the populations, and apparent functional advantages of asynchronous phasic firing.

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Simulating in vivo study of the effect of reduced dynorphin on burst and silence duration.Our data simulates in vivo analysis using data from multiple cells by generating 100 model cells with random variation in the seven non-synaptic parameters used to fit the varied cells in Figure 3 (Table 4). The dynorphin antagonist was simulated by reducing kD by 15% in each generated cell. This is sufficient to have a large effect on spike rate (A) and the burst duration histogram and hazard (B), comparable to the in vivo results in Brown et al 2006 [28] (c.f. their figure 5) but also shows very little effect on the silence (inter-burst interval) duration (C), similar to what they observe, suggesting that their results do not, as they interpret, exclude a role for dynorphin in generating inter-burst silence.
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pcbi-1002740-g008: Simulating in vivo study of the effect of reduced dynorphin on burst and silence duration.Our data simulates in vivo analysis using data from multiple cells by generating 100 model cells with random variation in the seven non-synaptic parameters used to fit the varied cells in Figure 3 (Table 4). The dynorphin antagonist was simulated by reducing kD by 15% in each generated cell. This is sufficient to have a large effect on spike rate (A) and the burst duration histogram and hazard (B), comparable to the in vivo results in Brown et al 2006 [28] (c.f. their figure 5) but also shows very little effect on the silence (inter-burst interval) duration (C), similar to what they observe, suggesting that their results do not, as they interpret, exclude a role for dynorphin in generating inter-burst silence.

Mentions: In the model, one of the main elements which generates the inter-burst silence is a dynorphin driven hyperpolarisation. In addition to dynorphin, silence duration is also determined by the AHP and the synaptic input rate. However, Brown et al [28] studied the effect of the competitive dynorphin antagonist nor-BNI, combining analysis of in vitro and in vivo results to suggest that dynorphin does not affect inter-burst activity. We used the model to test this by simulating their in vivo results. As a competitive antagonist, nor-BNI reduces rather than blocks dynorphin's effect and we simulated this in the model by reducing parameter kD (eqn. 7) sufficiently to produce a similar increase in burst duration as they observe. Their in vivo data is taken from multiple cells, and to simulate cell variation we generated 100 model cells based on the fit to cell v1, with small random variations in the parameter subset we varied to fit different in vivo cells (Table 4). We ran each cell for 3000 s with and without reduced kD, generating a total of 3032 bursts under simulated control, and 1239 bursts under simulated nor-BNI. We then generated the same collected data burst and silence duration histograms and hazards as shown in Figure 5 of Brown et al 2006 [28] (Figure 8).


Phasic firing in vasopressin cells: understanding its functional significance through computational models.

MacGregor DJ, Leng G - PLoS Comput. Biol. (2012)

Simulating in vivo study of the effect of reduced dynorphin on burst and silence duration.Our data simulates in vivo analysis using data from multiple cells by generating 100 model cells with random variation in the seven non-synaptic parameters used to fit the varied cells in Figure 3 (Table 4). The dynorphin antagonist was simulated by reducing kD by 15% in each generated cell. This is sufficient to have a large effect on spike rate (A) and the burst duration histogram and hazard (B), comparable to the in vivo results in Brown et al 2006 [28] (c.f. their figure 5) but also shows very little effect on the silence (inter-burst interval) duration (C), similar to what they observe, suggesting that their results do not, as they interpret, exclude a role for dynorphin in generating inter-burst silence.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475655&req=5

pcbi-1002740-g008: Simulating in vivo study of the effect of reduced dynorphin on burst and silence duration.Our data simulates in vivo analysis using data from multiple cells by generating 100 model cells with random variation in the seven non-synaptic parameters used to fit the varied cells in Figure 3 (Table 4). The dynorphin antagonist was simulated by reducing kD by 15% in each generated cell. This is sufficient to have a large effect on spike rate (A) and the burst duration histogram and hazard (B), comparable to the in vivo results in Brown et al 2006 [28] (c.f. their figure 5) but also shows very little effect on the silence (inter-burst interval) duration (C), similar to what they observe, suggesting that their results do not, as they interpret, exclude a role for dynorphin in generating inter-burst silence.
Mentions: In the model, one of the main elements which generates the inter-burst silence is a dynorphin driven hyperpolarisation. In addition to dynorphin, silence duration is also determined by the AHP and the synaptic input rate. However, Brown et al [28] studied the effect of the competitive dynorphin antagonist nor-BNI, combining analysis of in vitro and in vivo results to suggest that dynorphin does not affect inter-burst activity. We used the model to test this by simulating their in vivo results. As a competitive antagonist, nor-BNI reduces rather than blocks dynorphin's effect and we simulated this in the model by reducing parameter kD (eqn. 7) sufficiently to produce a similar increase in burst duration as they observe. Their in vivo data is taken from multiple cells, and to simulate cell variation we generated 100 model cells based on the fit to cell v1, with small random variations in the parameter subset we varied to fit different in vivo cells (Table 4). We ran each cell for 3000 s with and without reduced kD, generating a total of 3032 bursts under simulated control, and 1239 bursts under simulated nor-BNI. We then generated the same collected data burst and silence duration histograms and hazards as shown in Figure 5 of Brown et al 2006 [28] (Figure 8).

Bottom Line: By comparison with the non-phasic population, the phasic population responds linearly to increases in tonic synaptic input.Non-phasic cells respond to transient elevations in synaptic input in a way that strongly depends on background activity levels, phasic cells in a way that is independent of background levels, and show a similar strong linearization of the response.These findings show large differences in information coding between the populations, and apparent functional advantages of asynchronous phasic firing.

View Article: PubMed Central - PubMed

Affiliation: Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Vasopressin neurons, responding to input generated by osmotic pressure, use an intrinsic mechanism to shift from slow irregular firing to a distinct phasic pattern, consisting of long bursts and silences lasting tens of seconds. With increased input, bursts lengthen, eventually shifting to continuous firing. The phasic activity remains asynchronous across the cells and is not reflected in the population output signal. Here we have used a computational vasopressin neuron model to investigate the functional significance of the phasic firing pattern. We generated a concise model of the synaptic input driven spike firing mechanism that gives a close quantitative match to vasopressin neuron spike activity recorded in vivo, tested against endogenous activity and experimental interventions. The integrate-and-fire based model provides a simple physiological explanation of the phasic firing mechanism involving an activity-dependent slow depolarising afterpotential (DAP) generated by a calcium-inactivated potassium leak current. This is modulated by the slower, opposing, action of activity-dependent dendritic dynorphin release, which inactivates the DAP, the opposing effects generating successive periods of bursting and silence. Model cells are not spontaneously active, but fire when perturbed by random perturbations mimicking synaptic input. We constructed one population of such phasic neurons, and another population of similar cells but which lacked the ability to fire phasically. We then studied how these two populations differed in the way that they encoded changes in afferent inputs. By comparison with the non-phasic population, the phasic population responds linearly to increases in tonic synaptic input. Non-phasic cells respond to transient elevations in synaptic input in a way that strongly depends on background activity levels, phasic cells in a way that is independent of background levels, and show a similar strong linearization of the response. These findings show large differences in information coding between the populations, and apparent functional advantages of asynchronous phasic firing.

Show MeSH
Related in: MedlinePlus