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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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Analysis of in vivo phasic firing activity.(A) The 1-s bin spike rate counts for a phasic firing vasopressin neuron recorded in rat supraoptic nucleus. The cell fires in long bursts of varying duration (here mean 154s) which begin with a distinct peak before falling to a more stable intraburst firing rate (mean 8.91Hz). Bursts are separated by silent periods of almost no firing, and more regular duration (here mean 16s). (B) The burst profile of a cell is characterised by examining the mean firing rate over the first 50s and last 50s across all bursts, defining a mean shape for the head and tail. At the start of a burst, the firing rate rises rapidly, over ∼3s before falling back to a relatively stable plateau. The burst tail shows a slightly less rapid shift, declining over ∼7s. (C) The ISI histogram and hazard function show the short term spike patterning and post-spike excitability. The lack of short intervals is due to the HAP dominated refractory period. The following peak in excitability indicates a DAP. Beyond ∼150ms, the tail of the histogram can be fitted by a decaying exponential (y = 5000e−0.0114x, r2 = 0.982), indicating that firing is otherwise random. The single spike effect of the AHP is too small to distinguish here [24].
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pcbi-1002740-g002: Analysis of in vivo phasic firing activity.(A) The 1-s bin spike rate counts for a phasic firing vasopressin neuron recorded in rat supraoptic nucleus. The cell fires in long bursts of varying duration (here mean 154s) which begin with a distinct peak before falling to a more stable intraburst firing rate (mean 8.91Hz). Bursts are separated by silent periods of almost no firing, and more regular duration (here mean 16s). (B) The burst profile of a cell is characterised by examining the mean firing rate over the first 50s and last 50s across all bursts, defining a mean shape for the head and tail. At the start of a burst, the firing rate rises rapidly, over ∼3s before falling back to a relatively stable plateau. The burst tail shows a slightly less rapid shift, declining over ∼7s. (C) The ISI histogram and hazard function show the short term spike patterning and post-spike excitability. The lack of short intervals is due to the HAP dominated refractory period. The following peak in excitability indicates a DAP. Beyond ∼150ms, the tail of the histogram can be fitted by a decaying exponential (y = 5000e−0.0114x, r2 = 0.982), indicating that firing is otherwise random. The single spike effect of the AHP is too small to distinguish here [24].

Mentions: To match the firing activity of a cell with a model in a quantitatively robust way, we need to define the features of that activity in a way that we can statistically compare recorded and model generated data. Spike activity comprises the overall firing pattern, shown by spike rate as measured in bins of varying width; the bursting characteristics, using burst detection to quantify bursts and silences and plot the mean burst profile; and short term spike patterning and excitability, contained in the ISI distribution and related hazard function (Figure 2). Together, these measures capture the important features of phasic firing, and describe the variation between individual cells. They also relate closely to many of the underlying mechanisms, such as the post-spike potentials.


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

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

Analysis of in vivo phasic firing activity.(A) The 1-s bin spike rate counts for a phasic firing vasopressin neuron recorded in rat supraoptic nucleus. The cell fires in long bursts of varying duration (here mean 154s) which begin with a distinct peak before falling to a more stable intraburst firing rate (mean 8.91Hz). Bursts are separated by silent periods of almost no firing, and more regular duration (here mean 16s). (B) The burst profile of a cell is characterised by examining the mean firing rate over the first 50s and last 50s across all bursts, defining a mean shape for the head and tail. At the start of a burst, the firing rate rises rapidly, over ∼3s before falling back to a relatively stable plateau. The burst tail shows a slightly less rapid shift, declining over ∼7s. (C) The ISI histogram and hazard function show the short term spike patterning and post-spike excitability. The lack of short intervals is due to the HAP dominated refractory period. The following peak in excitability indicates a DAP. Beyond ∼150ms, the tail of the histogram can be fitted by a decaying exponential (y = 5000e−0.0114x, r2 = 0.982), indicating that firing is otherwise random. The single spike effect of the AHP is too small to distinguish here [24].
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002740-g002: Analysis of in vivo phasic firing activity.(A) The 1-s bin spike rate counts for a phasic firing vasopressin neuron recorded in rat supraoptic nucleus. The cell fires in long bursts of varying duration (here mean 154s) which begin with a distinct peak before falling to a more stable intraburst firing rate (mean 8.91Hz). Bursts are separated by silent periods of almost no firing, and more regular duration (here mean 16s). (B) The burst profile of a cell is characterised by examining the mean firing rate over the first 50s and last 50s across all bursts, defining a mean shape for the head and tail. At the start of a burst, the firing rate rises rapidly, over ∼3s before falling back to a relatively stable plateau. The burst tail shows a slightly less rapid shift, declining over ∼7s. (C) The ISI histogram and hazard function show the short term spike patterning and post-spike excitability. The lack of short intervals is due to the HAP dominated refractory period. The following peak in excitability indicates a DAP. Beyond ∼150ms, the tail of the histogram can be fitted by a decaying exponential (y = 5000e−0.0114x, r2 = 0.982), indicating that firing is otherwise random. The single spike effect of the AHP is too small to distinguish here [24].
Mentions: To match the firing activity of a cell with a model in a quantitatively robust way, we need to define the features of that activity in a way that we can statistically compare recorded and model generated data. Spike activity comprises the overall firing pattern, shown by spike rate as measured in bins of varying width; the bursting characteristics, using burst detection to quantify bursts and silences and plot the mean burst profile; and short term spike patterning and excitability, contained in the ISI distribution and related hazard function (Figure 2). Together, these measures capture the important features of phasic firing, and describe the variation between individual cells. They also relate closely to many of the underlying mechanisms, such as the post-spike potentials.

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