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Related Concept Videos

Neural Circuits01:25

Neural Circuits

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...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...

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Related Experiment Videos

Sigmoid neural transfer function realized by percolation.

D Pignon, P J Parmiter, J K Slack

    Optics Letters
    |October 30, 2009
    PubMed
    Summary
    This summary is machine-generated.

    Researchers demonstrated neural functionality using percolation, an analog computing approach. This novel method uses amorphous silicon and optical inputs to create stochastic bit streams for neuron output, mimicking digital processes.

    Related Experiment Videos

    Area of Science:

    • Neuroscience
    • Computer Science
    • Materials Science

    Background:

    • Neural networks require complex computational functions like summing and sigmoid transfer.
    • Implementing these functions efficiently in hardware is a key challenge in neuromorphic computing.

    Purpose of the Study:

    • To demonstrate neural functionality, specifically summing and sigmoid transfer, using the phenomenon of percolation.
    • To present a novel analog approximation to digital percolation for neural computation.

    Main Methods:

    • An experiment was designed utilizing the percolation phenomenon.
    • A device was constructed from amorphous silicon with stochastic bit-stream optical inputs.
    • Neuron output was defined by percolating current, generating a stochastic bit stream.

    Main Results:

    • Preliminary experimental results demonstrating the feasibility of the approach were obtained.
    • The device successfully approximated analog to digital percolation.
    • Stochastic bit streams were generated for both input and output.

    Conclusions:

    • Percolation offers a viable physical phenomenon for implementing neural functions.
    • The developed analog device shows promise for efficient neuromorphic computing.
    • Further research is warranted to optimize and scale this percolation-based neural implementation.