2014
DOI: 10.1016/j.neuron.2014.04.002
|View full text |Cite|
|
Sign up to set email alerts
|

Abstract: SUMMARY How do neurons develop, control, and maintain their electrical signaling properties in spite of ongoing protein turnover and perturbations to activity? From generic assumptions about the molecular biology underlying channel expression, we derive a simple model and show how it encodes an “activity set point” in single neurons. The model generates diverse self-regulating cell types and relates correlations in conductance expression observed in vivo to underlying channel expression rates. Synaptic as well… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

11
338
4
1

Year Published

2014
2014
2022
2022

Publication Types

Select...
7
1

Relationship

1
7

Authors

Journals

citations
Cited by 264 publications
(354 citation statements)
references
References 77 publications
(142 reference statements)
11
338
4
1
Order By: Relevance
“…This result suggests that the population activity reached a set point of activity that was necessary for the PPC’s role in the task. This finding is conceptually similar to the homeostatic properties of the stomatogastric ganglion (O’Leary et al, 2014; Prinz et al, 2004). In that system, the same firing patterns of neurons can be achieved through different combinations of ion channels and ion channel expression levels.…”
Section: Discussionsupporting
confidence: 76%
“…This result suggests that the population activity reached a set point of activity that was necessary for the PPC’s role in the task. This finding is conceptually similar to the homeostatic properties of the stomatogastric ganglion (O’Leary et al, 2014; Prinz et al, 2004). In that system, the same firing patterns of neurons can be achieved through different combinations of ion channels and ion channel expression levels.…”
Section: Discussionsupporting
confidence: 76%
“…This kind of variability is not noise; it represents genuinely different parameter combinations that the biological system has found. For this reason, understanding the regulatory logic of the nervous system is of fundamental importance [44**]. …”
Section: Relating Data To Modelsmentioning
confidence: 99%
“…A recent study by O’Donnell and Sejnowski [58*] shows that memory generalization can emerge from diffusion of plasticity proteins in dendritic trees. Similarly, a coarse model of activity dependent ion channel regulation has recently helped explain physiologically important expression patterns in the mRNA of ion channels in identified neurons, while accounting for cell-to-cell variability [44**,59]. Building more realistic and detailed molecular models is becoming more feasible as imaging and subcellular biochemistry are providing more data to constrain these models [60], but there will always be a role for conceptual models – especially in gaining intuition and in situations where data-fitting is impractical for reasons we have already discussed.…”
Section: Conceptual Models As Tools For Explaining Data and Asking “Wmentioning
confidence: 99%
“…Straight lines are calculated from the ratio of regulation time-constants for each pair of conductances in each cell type. Adapted from O'Leary et al (2014).…”
Section: Figurementioning
confidence: 99%
“…Past models of homeostatic regulation of activity type have not explicitly observed or sought such correlations, until recently when O'Leary and colleagues (O'Leary, 2013) made a simple neuronal model that showed how abstract ‘regulation’ time-constants determine correlations in conductance expression at steady state. In the current issue of Neuron, O'Leary and colleagues (O'Leary, 2014) extend and transform that initial model by going back to basics.…”
mentioning
confidence: 99%