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1Encoding of sound-source elevation by the spike patterns of cortical neurons

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“Encoding of sound-source elevation by the spike patterns of cortical neurons” Metadata:

  • Title: ➤  Encoding of sound-source elevation by the spike patterns of cortical neurons
  • Author:
  • Language: English
  • Number of Pages: Median: 140
  • Publisher: Gainesville, FL
  • Publish Date:

“Encoding of sound-source elevation by the spike patterns of cortical neurons” Subjects and Themes:

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Access and General Info:

  • First Year Published: 1999
  • Is Full Text Available: Yes
  • Is The Book Public: Yes
  • Access Status: Public

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    Neural network (machine learning)

    model inspired by the structure and functions of biological neural networks. A neural network consists of connected units or nodes called artificial neurons

    Feedforward neural network

    Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights

    Neural network (biology)

    Biological neural networks are studied to understand the organization and functioning of nervous systems. Closely related are artificial neural networks, machine

    Deep learning

    networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance

    Quantum neural network

    Quantum neural networks are computational neural network models which are based on the principles of quantum mechanics. The first ideas on quantum neural computation

    Neural processing unit

    (AI) and machine learning applications, including artificial neural networks and computer vision. Their purpose is either to efficiently execute already

    Residual neural network

    training and convergence of deep neural networks with hundreds of layers, and is a common motif in deep neural networks, such as transformer models (e.g

    Types of artificial neural networks

    types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate

    Differentiable neural computer

    In artificial intelligence, a differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (but not

    Convolutional neural network

    processing, brain–computer interfaces, and financial time series. CNNs are also known as shift invariant or space invariant artificial neural networks, based on


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