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Phasic Firing in Vasopressin Cells: Understanding Its Functional Significance through Computational Models

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Phasic Firing in Vasopressin Cells: Understanding Its Functional Significance through Computational Models
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002740
Pubmed ID
Authors

Duncan J. MacGregor, Gareth Leng

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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Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 25%
Student > Ph. D. Student 6 21%
Researcher 6 21%
Student > Master 3 11%
Student > Doctoral Student 1 4%
Other 1 4%
Unknown 4 14%
Readers by discipline Count As %
Neuroscience 9 32%
Agricultural and Biological Sciences 4 14%
Physics and Astronomy 2 7%
Mathematics 2 7%
Engineering 2 7%
Other 3 11%
Unknown 6 21%