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Related Concept Videos

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.
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Neural Circuits01:25

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Anatomy of the Eyeball01:20

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...
Second Order systems II01:18

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
If  ζ...
Second Order systems I01:20

Second Order systems I

A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...

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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Optical implementation of a second-order neural network.

L Zhang, M G Robinson, K M Johnson

    Optics Letters
    |September 24, 2009
    PubMed
    Summary
    This summary is machine-generated.

    This study presents an optical neural network that uses ferroelectric liquid-crystal spatial light modulators to perform pattern association and learn tasks. The system demonstrates translation invariance and tolerance to noise and contrast variations.

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    Area of Science:

    • Optoelectronics
    • Artificial Neural Networks
    • Computational Neuroscience

    Background:

    • Optical implementations of neural networks offer potential for high-speed parallel processing.
    • Second-order neural networks, capable of learning quadratic relationships, are computationally intensive for electronic realization.

    Purpose of the Study:

    • To demonstrate a single-layer, second-order neural network using optical components.
    • To evaluate the system's capability for pattern association, learning, and translation invariance.
    • To assess the tolerance of the optical neural network to input contrast and electronic noise.

    Main Methods:

    • Utilized a single-layer, second-order neural network architecture.
    • Employed ferroelectric liquid-crystal (FLC) spatial light modulators (SLMs) for input and interconnection weights.
    • Optical beam passed twice through an input FLC SLM to obtain quadratic products.
    • A 128x128 FLC SLM implemented interconnection weights.

    Main Results:

    • Successfully associated eight random pattern-target pairs (16 and 4 dimensions).
    • Demonstrated the network's ability to learn parity association.
    • Exhibited translation invariance in pattern recognition.
    • Computer modeling indicated tolerance to 4:1 input SLM contrast ratios and 10% electronic noise.

    Conclusions:

    • The presented optical implementation is a viable approach for second-order neural networks.
    • The system shows promise for high-speed associative learning and pattern recognition tasks.
    • The demonstrated robustness to noise and contrast suggests practical applicability.