Back

Estimates of quantal synaptic parameters in light of more complex vesicle pool models

Bornschein, G.; Brachtendorf, S.; Schmidt, H.

2024-12-17 neuroscience
10.1101/2024.12.13.628305 bioRxiv
Show abstract

The subdivision of synaptic vesicles (SVs) into discrete pools is a leading concept of synaptic physiology. To better explain specific properties of transmission and plasticity, it has been suggested initially that the readily releasable pool (RRP) of SVs is subdivided into two parallel pools differing in their release probability. More recently, evidence was provided that sequential pools with a single RRP and a series-connected finite-size replacement pool (RP) inserted between the reserve pool (RSP) and RRP equally well or even better account for most aspects of transmission and plasticity. It was further suggest that a fraction of the presynaptic release sites (N) are initially unoccupied by SVs, with vesicle recruitment occurring rapidly during activity, and furthermore that the number of release sites itself changes with rapid dynamics during activity. Here we propose a framework that identifies specific signs of the presence of the series-connected RP, using a combination of two experimental electrophysiological standard methods, cumulative analysis (CumAna) and multiple probability fluctuation analysis (MPFA). In particular we show that if the y-intercept (y(0)) of CumAna is larger than N reported by MPFA (y(0) > NMPFA) this is a strong indication for a series-connected RP. This is due to the fact that y(0) reports the sum of RRP and RP. Our analysis further suggests that this result is not affected by unoccupied release sites, as such empty sites contribute to both estimates, y(0) and NMPFA. We discuss experimental findings and models in the recent literature in the light of our theoretical considerations.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
PLOS Computational Biology
1633 papers in training set
Top 0.7%
22.6%
2
Journal of Computational Neuroscience
23 papers in training set
Top 0.1%
17.6%
3
Scientific Reports
3102 papers in training set
Top 18%
6.3%
4
Frontiers in Physiology
93 papers in training set
Top 1%
3.6%
50% of probability mass above
5
Neuroscience
88 papers in training set
Top 0.3%
3.6%
6
PLOS ONE
4510 papers in training set
Top 39%
3.6%
7
Chaos, Solitons & Fractals
32 papers in training set
Top 0.7%
2.6%
8
eneuro
389 papers in training set
Top 4%
2.4%
9
Frontiers in Neural Circuits
36 papers in training set
Top 0.2%
2.1%
10
Frontiers in Neuroscience
223 papers in training set
Top 4%
1.7%
11
Brain Sciences
52 papers in training set
Top 0.7%
1.7%
12
Frontiers in Cellular Neuroscience
79 papers in training set
Top 0.5%
1.7%
13
Neuroinformatics
40 papers in training set
Top 0.6%
1.5%
14
Frontiers in Neuroinformatics
38 papers in training set
Top 0.5%
1.3%
15
International Journal of Molecular Sciences
453 papers in training set
Top 12%
1.0%
16
Journal of Neurophysiology
263 papers in training set
Top 0.7%
0.9%
17
Neural Computation
36 papers in training set
Top 0.6%
0.8%
18
Journal of Neurochemistry
50 papers in training set
Top 0.5%
0.8%
19
Biological Cybernetics
12 papers in training set
Top 0.2%
0.7%
20
Biophysical Journal
545 papers in training set
Top 5%
0.7%
21
Biology
43 papers in training set
Top 3%
0.7%
22
eLife
5422 papers in training set
Top 58%
0.7%
23
Frontiers in Systems Neuroscience
19 papers in training set
Top 0.5%
0.7%
24
Neural Networks
32 papers in training set
Top 0.9%
0.7%
25
Frontiers in Computational Neuroscience
53 papers in training set
Top 2%
0.6%