Fano factor reduction on the 0.7 structure

2005 ◽  
Author(s):  
P. Roche
Keyword(s):  
Fano Factor ◽  
0.7 Structure

Fano Factor Fact and Fallacy

1970 ◽  
Vol 17 (3) ◽  
pp. 187-195 ◽  
Author(s):  
H. R. Zulliger ◽  
D. W. Aitken
Keyword(s):  
Fano Factor

Fano factor evaluation of diamond detectors for alpha particles

2016 ◽  
Vol 213 (10) ◽  
pp. 2629-2633 ◽  
Author(s):  
Takehiro Shimaoka ◽  
Junichi H. Kaneko ◽  
Yuki Sato ◽  
Masakatsu Tsubota ◽  
Hiroaki Shimmyo ◽  
...  
Keyword(s):  
Fano Factor ◽  
Alpha Particles ◽  
Factor Evaluation

scholarly journals Fano factor and the mean energy per ion pair in counting gases, at low x-ray energies

1997 ◽  
Vol 82 (2) ◽  
pp. 871-877 ◽  
Author(s):  
A. Pansky ◽  
A. Breskin ◽  
R. Chechik
Keyword(s):  
Fano Factor ◽  
Ion Pair ◽  
X Ray ◽  
The Mean ◽  
Low X

Correlated Firing Improves Stimulus Discrimination in a Retinal Model

2004 ◽  
Vol 16 (11) ◽  
pp. 2261-2291 ◽  
Author(s):  
Garrett T. Kenyon ◽  
James Theiler ◽  
John S. George ◽  
Bryan J. Travis ◽  
David W. Marshak
Keyword(s):  
Stimulus Intensity ◽  
Ganglion Cells ◽  
Fano Factor ◽  
Spike Trains ◽  
Dependent Manner ◽  
Common Input ◽  
Threshold Detector ◽  
Synchronous Firing ◽  
Inhibitory Feedback ◽  
Synchronous Oscillations

Synchronous firing limits the amount of information that can be extracted by averaging the firing rates of similarly tuned neurons. Here, we show that the loss of such rate-coded information due to synchronous oscillations between retinal ganglion cells can be overcome by exploiting the information encoded by the correlations themselves. Two very different models, one based on axon-mediated inhibitory feedback and the other on oscillatory common input, were used to generate artificial spike trains whose synchronous oscillations were similar to those measured experimentally. Pooled spike trains were summed into a threshold detector whose output was classified using Bayesian discrimination. For a threshold detector with short summation times, realistic oscillatory input yielded superior discrimination of stimulus intensity compared to rate-matched Poisson controls. Even for summation times too long to resolve synchronous inputs, gamma band oscillations still contributed to improved discrimination by reducing the total spike count variability, or Fano factor. In separate experiments in which neurons were synchronized in a stimulus-dependent manner without attendant oscillations, the Fano factor increased markedly with stimulus intensity, implying that stimulus-dependent oscillations can offset the increased variability due to synchrony alone.

scholarly journals Recurrence time distribution and temporal clustering properties of a cellular automaton modelling landslide events

2013 ◽  
Vol 20 (6) ◽  
pp. 1071-1078 ◽  
Author(s):  
E. Piegari ◽  
R. Di Maio ◽  
A. Avella
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Cellular Automaton ◽  
Time Window ◽  
Fano Factor ◽  
Recent Analysis ◽  
Recurrence Time ◽  
Model Parameters ◽  
Reasonable Prediction ◽  
First Time

Abstract. Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

scholarly journals A modified fluctuation test for elucidating drug resistance in microbial and cancer cells

2020 ◽  
Author(s):  
Pavol Bokes ◽  
Abhyudai Singh
Keyword(s):  
Drug Resistance ◽  
Cancer Cells ◽  
Colony Size ◽  
Random Number ◽  
Initial Conditions ◽  
Fano Factor ◽  
Persister Cells ◽  
Efficient Treatment ◽  
Fluctuation Test ◽  
The Mean

AbstractClonal populations of microbial and cancer cells are often driven into a drug-tolerant persister state in response to drug therapy, and these persisters can subsequently adapt to the new drug environment via genetic and epigenetic mechanisms. Estimating the frequency with which drug-tolerance states arise, and its transition to drug-resistance, is critical for designing efficient treatment schedules. Here we study a stochastic model of cell proliferation where drug-tolerant persister cells transform into a drug-resistant state with a certain adaptation rate, and the resistant cells can then proliferate in the presence of the drug. Assuming a random number of persisters to begin with, we derive an exact analytical expression for the statistical moments and the distribution of the total cell count (i.e., colony size) over time. Interestingly, for Poisson initial conditions the noise in the colony size (as quantified by the Fano factor) becomes independent of the initial condition and only depends on the adaptation rate. Thus, experimentally quantifying the fluctuations in the colony sizes provides an estimate of the adaptation rate, which then can be used to infer the starting persister numbers from the mean colony size. Overall, our analysis introduces a modification of the classical Luria–Delbrück experiment, also called the “Fluctuation Test”, providing a valuable tool to quantify the emergence of drug resistance in cell populations.

How to Determine Experimentally the Fano Factor in DROIDs

2009 ◽  
Author(s):  
Victor V. Samedov ◽  
Betty Young ◽  
Blas Cabrera ◽  
Aaron Miller
Keyword(s):  
Fano Factor

Research on Fano Factor for Pseudo-Thermal Light Field Based on Multi-Pixel Photon Counter

2011 ◽  
Vol 38 (10) ◽  
pp. 1008010
Author(s):  
张合勇 Zhang Heyong ◽  
郭劲 Guo Jin ◽  
赵帅 Zhao Shuai ◽  
王挺峰 Wang Tingfeng ◽  
刘立生 Liu Lisheng
Keyword(s):  
Light Field ◽  
Fano Factor ◽  
Photon Counter ◽  
Thermal Light
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