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相关概念视频

Auditory Pathway01:15

Auditory Pathway

4.8K
Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
When viewed cross-sectionally, the cochlea reveals the scala vestibuli and scala tympani flanking...
4.8K
Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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相关实验视频

Updated: Jun 6, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

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深度神经网络解释了听觉皮层中的尖端活动.

Bilal Ahmed1, Joshua D Downer2, Brian J Malone2,3

  • 1Elmore School of Electrical and Computer Engineering, Purdue University.

bioRxiv : the preprint server for biology
|November 28, 2024
PubMed
概括
此摘要是机器生成的。

训练在语音上的人工神经网络 (ANN) 能够在精细的时间尺度上预测听力皮层中的神经活动. 这些ANN在解释神经对听觉刺激的反应方面优于传统模型.

关键词:
人工神经网络的人工神经网络听觉皮层的听觉皮层.自动语音识别自动语音识别灵长类的灵长类动物.

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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
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Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat
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Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat

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Last Updated: Jun 6, 2025

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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
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科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 审计处理 审计处理

背景情况:

  • 人工神经网络 (ANN) 擅长预测灵长类动物视觉和听觉皮层对静态刺激的神经反应,并且在较慢的时间尺度上.
  • 在听力皮层中预测细粒度尖端活动的ANN的有效性,对于处理语音等动态声音至关重要,仍然在很大程度上未被探索.

研究的目的:

  • 调查在语音音频中训练有素的ANN是否可以预测听力皮层中神经尖端活动在精细的时间尺度 (50毫秒及以下) 中.
  • 为了比较训练有素的ANN的预测能力与传统的光谱-时间-感受场模型和未经训练的网络.

主要方法:

  • 利用了在语音音频数据集上训练的ANN.
  • 从松鼠子的听觉皮层进行了急性多电极记录.
  • 分析了神经尖峰数量,以响应使用不同时间区宽度的语音和子发音.

主要成果:

  • 训练有素的ANN在50毫秒以下的时间尺度上成功预测了听觉皮层中神经元峰值的数量.
  • 与传统的光谱-时间-感受场模型和未训练的网络相比,ANN解释了显著更多可解释的神经变异.
  • 更深层的ANN层显示出非初级神经元的更好预测,表明层特定的处理,尽管观察到显著的神经元到神经元的变化.

结论:

  • 训练有素的ANN是模拟听觉皮层中细粒度神经动态的强大工具,将其预测成功扩展到快速的时间处理.
  • 这些发现突出了ANN在听觉中揭示复杂的神经编码策略的潜力,这些可能会被粗略的分析方法遗漏.