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Related Concept Videos

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
Classification of Systems-I01:26

Classification of Systems-I

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Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Linearization and Approximation01:26

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Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

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Related Experiment Video

Updated: May 31, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Characterization of minimum error linear coding with sensory and neural noise.

Eizaburo Doi1, Michael S Lewicki

  • 1Center for Neural Science, New York University, New York, NY 10003, USA. edoi@cns.nyu.edu

Neural Computation
|July 8, 2011
PubMed
Summary

This study introduces a new method for robust coding to minimize errors in neural decoding, even with degraded input signals. The optimal linear encoder is split into Wiener filtering and robust coding for separate optimization.

Related Experiment Videos

Last Updated: May 31, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Computational Neuroscience
  • Information Theory
  • Signal Processing

Background:

  • Robust coding aims to reduce decoding errors caused by neural noise.
  • Real-world sensory coding involves both internal neural noise and external signal degradation (e.g., blurring).

Purpose of the Study:

  • To generalize robust coding to scenarios with input signal degradation.
  • To decompose the optimal linear encoder for this generalized problem into optimizable components.

Main Methods:

  • Decomposition of the optimal linear encoder into two serial processes: Wiener filtering and robust coding.
  • Spectral analysis to characterize error minimization under varying conditions.

Main Results:

  • The optimal linear encoder can be precisely decomposed into Wiener filtering (for input compensation) and robust coding (for noisy neural transmission).
  • Spectral analysis reveals how reconstruction error is minimized based on signal spectra, degradation, neural precision, and population size.

Conclusions:

  • The proposed decomposition provides an effective strategy for optimizing sensory coding in the presence of both internal and external noise.
  • This framework offers insights into biological sensory systems and informs the design of artificial sensory coding strategies.