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Small-signal Diode Model01:18

Small-signal Diode Model

In analyzing the behavior of diodes in circuits, the relationship between the current through a diode and the voltage across it is of particular interest, especially when considering the effect of a direct current (DC) bias voltage. When applied, this DC bias influences the diode's operating point, known as the Q point, around which the current-voltage (I-V) characteristic of the diode exhibits exponential behavior. Introducing a small, time-varying signal on top of this bias aids in examining...
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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
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Related Experiment Video

Updated: Jun 8, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

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Information capacity in the weak-signal approximation.

Lubomir Kostal1

  • 1Institute of Physiology AS CR,142 20 Prague 4, Czech Republic. kostal@biomed.cas.cz

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 28, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an approximation for mutual information in continuous-input channels, showing input correlations impact capacity in memoryless channels but not in channels with memory. Matching input covariances to noise structure can enable near-noiseless information transfer.

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Area of Science:

  • Information Theory
  • Signal Processing
  • Mathematical Neuroscience

Background:

  • Continuous-time stationary channels with vanishing input amplitude/power present challenges for information transfer analysis.
  • Understanding mutual information is crucial for optimizing communication systems and analyzing biological processes.

Purpose of the Study:

  • To derive an approximate expression for mutual information in discrete-time stationary channels with continuous input.
  • To analyze the impact of input correlations and channel properties on information transfer under weak-signal conditions.

Main Methods:

  • Developed an approximation for mutual information using input covariance matrices and Fisher information matrices.
  • Analyzed optimality conditions by separating input and channel properties.
  • Investigated the effect of input correlations on channel capacity in memoryless and memory channels.

Main Results:

  • Input correlations do not affect channel capacity in memoryless channels under vanishing input amplitude/power.
  • For channels with memory, matching input covariances to noise structure can achieve near-noiseless information transfer.
  • The approximation is particularly relevant for high-noise, weak-signal regimes common in biophysics and neuroscience.

Conclusions:

  • The derived approximation provides a convenient framework for analyzing information transfer in various channel types.
  • Optimizing input covariances is key to maximizing information transfer in channels with memory.
  • Results offer potential insights for modeling information processing in biological systems.