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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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深度学习模型用于非典型的血清细胞识别.

Daniele Corradetti1, Alessandro Bernardi2, Renato Corradetti3

  • 1Grupo de Fisica Matematica, Instituto Superior Tecnico, Av. Rovisco Pais, Lisboa, 1049-001, Portugal; Departamento de Matematica, Universidade do Algarve, Campus de Gambelas, Faro, 8005-139, Faro, Portugal.

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概括

深度学习模型能够准确地识别典型和非典型的血清神经元,包括那些具有不寻常的发射模式的神经元. 这有助于研究大脑中不同类型的血清能神经元活动.

关键词:
卷积神经网络是一种卷积神经网络.深度学习模型深度学习模型背面的拉菲核核.血清增强神经元的神经元斯派克的识别方法

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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.
  • 机器学习 机器学习

背景情况:

  • 血清激素系统使用具有不同电生理学特性的多种神经元.
  • 目前的方法难以识别非典型的血清神经元,限制了对其功能的研究.

研究的目的:

  • 开发深度学习模型,以准确区分典型和非典型的血清性神经元与非血清性细胞.
  • 能够全面研究不同类型的血清性神经元活动的功能相关性.

主要方法:

  • 从已识别的 serotonergic 和非 serotonergic 神经元中利用了体外电生理学记录.
  • 采用卷积神经网络 (CNN) 来根据动作潜能特征进行分类.
  • 在大量真实和合成动作潜力样本的数据集上训练模型.

主要成果:

  • 深度学习模型在区分神经元副类型方面取得了高准确性.
  • 当模型在独立的,不均的数据集上进行验证时,模型显示出强度和可靠性.
  • 在识别包括非典型的神经元在内的神经元中获得了超过94%的准确性.

结论:

  • 开发的模型可靠地识别出典型和非典型的神经元.
  • 该模型已准备好用于实验,并可适应各种记录参数.
  • 促进了对神经生物学的作用的更广泛的研究. 血清激素系统多样性.