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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Related Experiment Video

Updated: Jun 7, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Optical implementation of a translation-invariant second-order neural network for multiple-pattern classification.

S Kakizaki, P Horan, A Arimoto

    Applied Optics
    |October 22, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an optical neural network for pattern recognition. An adaptive learning rule improves accuracy in recognizing numeric characters using a liquid-crystal display system.

    Related Experiment Videos

    Last Updated: Jun 7, 2026

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
    03:31

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

    Published on: December 15, 2023

    Area of Science:

    • Optoelectronics
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Optical implementation of neural networks presents challenges in achieving high accuracy for weighted interconnections.
    • Existing systems struggle with the precision required for complex pattern recognition tasks.

    Purpose of the Study:

    • To report a novel optical approach for second-order neural networks capable of multi-pattern recognition.
    • To address system issues, particularly the accuracy of weighted interconnections, in optical neural networks.
    • To demonstrate numeric character recognition using an experimental optical system.

    Main Methods:

    • Discussed system issues related to weighted interconnection accuracy for numeric character recognition (0-9).
    • Introduced an adaptive learning rule adjusting optical power during training to mitigate accuracy issues.
    • Utilized an experimental system featuring a liquid-crystal display for optical implementation.

    Main Results:

    • The accuracy of weighted interconnections significantly impacts neural network performance during the training phase more than during classification.
    • An adaptive learning rule was successfully implemented to adjust optical power during training.
    • Demonstrated successful numeric character recognition using the developed experimental optical system.

    Conclusions:

    • The proposed adaptive learning rule effectively addresses the accuracy challenges in optical neural network training.
    • The experimental system demonstrates the feasibility of optical second-order neural networks for practical pattern recognition tasks.
    • This approach offers a viable path for developing more robust and accurate optical pattern recognition systems.