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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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Related in: MedlinePlus

The model fitted to five typical phasic cells recorded in vivo.On the left we show pairs of matched in vivo and model generated spike rate data, and on the right, the fitted hazard and burst profiles. The model closely matches burst profile, mean burst length, mean silence length, intraburst firing rate, and the intraburst hazard, showing post-spike excitability and patterning. A subset of eight of the model's 21 parameters were varied to match the cells. The fit parameters vary synaptic input rate, HAP half life, AHP magnitude, fast DAP magnitude, dynorphin magnitude, calcium magnitude and K+ leak conductance. The parameter values are given in tables 1 and 2.
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pcbi-1002740-g003: The model fitted to five typical phasic cells recorded in vivo.On the left we show pairs of matched in vivo and model generated spike rate data, and on the right, the fitted hazard and burst profiles. The model closely matches burst profile, mean burst length, mean silence length, intraburst firing rate, and the intraburst hazard, showing post-spike excitability and patterning. A subset of eight of the model's 21 parameters were varied to match the cells. The fit parameters vary synaptic input rate, HAP half life, AHP magnitude, fast DAP magnitude, dynorphin magnitude, calcium magnitude and K+ leak conductance. The parameter values are given in tables 1 and 2.

Mentions: To understand which parameters are most important to variation between cells, we attempted to fit different cells by varying as few parameters as possible. This process identified the dynorphin parameters kD, λD, calcium parameter kC, and gL as those required to fit the burst measures. Parameter kD dominates the burst duration, while λD and gL can be adjusted to match varied silence durations. The HAP, AHP and DAP parameters were further adjusted to compensate for the K+ leak current's additional effect on short term spike patterning. The AHP parameter kAHP dominates the size of the peak in firing rate at the start of each burst, consistent with experimental evidence that this current is responsible [27]. Five representative fits to recorded phasic cells are shown in Figure 3, using the parameters given in Tables 1 and 2; all give a close match to the firing rate, hazard, and burst measures (Table 3).


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

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

The model fitted to five typical phasic cells recorded in vivo.On the left we show pairs of matched in vivo and model generated spike rate data, and on the right, the fitted hazard and burst profiles. The model closely matches burst profile, mean burst length, mean silence length, intraburst firing rate, and the intraburst hazard, showing post-spike excitability and patterning. A subset of eight of the model's 21 parameters were varied to match the cells. The fit parameters vary synaptic input rate, HAP half life, AHP magnitude, fast DAP magnitude, dynorphin magnitude, calcium magnitude and K+ leak conductance. The parameter values are given in tables 1 and 2.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002740-g003: The model fitted to five typical phasic cells recorded in vivo.On the left we show pairs of matched in vivo and model generated spike rate data, and on the right, the fitted hazard and burst profiles. The model closely matches burst profile, mean burst length, mean silence length, intraburst firing rate, and the intraburst hazard, showing post-spike excitability and patterning. A subset of eight of the model's 21 parameters were varied to match the cells. The fit parameters vary synaptic input rate, HAP half life, AHP magnitude, fast DAP magnitude, dynorphin magnitude, calcium magnitude and K+ leak conductance. The parameter values are given in tables 1 and 2.
Mentions: To understand which parameters are most important to variation between cells, we attempted to fit different cells by varying as few parameters as possible. This process identified the dynorphin parameters kD, λD, calcium parameter kC, and gL as those required to fit the burst measures. Parameter kD dominates the burst duration, while λD and gL can be adjusted to match varied silence durations. The HAP, AHP and DAP parameters were further adjusted to compensate for the K+ leak current's additional effect on short term spike patterning. The AHP parameter kAHP dominates the size of the peak in firing rate at the start of each burst, consistent with experimental evidence that this current is responsible [27]. Five representative fits to recorded phasic cells are shown in Figure 3, using the parameters given in Tables 1 and 2; all give a close match to the firing rate, hazard, and burst measures (Table 3).

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