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Related Concept Videos

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Estimating Population Mean with Unknown Standard Deviation01:22

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Parameter estimation and identifiability in a neural population model for electro-cortical activity.

Agus Hartoyo1, Peter J Cadusch2, David T J Liley3,4

  • 1Centre for Micro-Photonics, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.

Plos Computational Biology
|May 31, 2019
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Summary

This study reveals that fitting neural population models to electroencephalography (EEG) data is challenging. Only inhibitory synaptic activity dynamics are identifiable, suggesting a simplified model for brain activity analysis.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Brain Imaging

Background:

  • Electroencephalography (EEG) measures brain electrical activity non-invasively.
  • Neural population models simulate large neuronal networks to interpret EEG signals.
  • Tuning model parameters can replicate EEG features like the alpha-rhythm.

Purpose of the Study:

  • To address the unsolved inverse problem of estimating neural population model parameters directly from EEG data.
  • To develop a potential real-time method for characterizing average neuronal properties in humans.
  • To perform unbiased fits of a 22-parameter neural population model to EEG data.

Main Methods:

  • Utilized particle swarm optimization and Markov chain Monte Carlo sampling for model fitting.
  • Analyzed EEG data from 82 individuals using a 22-parameter neural population model.
  • Computed Kullback-Leibler divergences to quantify parameter identifiability.

Main Results:

  • Identified only one parameter, inhibitory synaptic activity dynamics, as directly identifiable from EEG data.
  • Observed large, correlated uncertainties for other model parameters.
  • Fisher information matrix eigenvalues indicated a 'sloppy' model with low effective dimensionality.

Conclusions:

  • Directly fitting complex neural population models to EEG data is limited in parameter identifiability.
  • Inhibitory synaptic activity plays a prominent role in driving system behavior within the model.
  • The model's 'sloppy' nature suggests potential for dimensionality reduction in analyzing brain activity.