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首页> 外文期刊>The European Journal of Neuroscience >Learning the invariance properties of complex cells from their responses to natural stimuli.
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Learning the invariance properties of complex cells from their responses to natural stimuli.

机译：从复杂细胞对自然刺激的反应中学习其不变性。

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Neurons in primary visual cortex are typically classified as either simple or complex. Whereas simple cells respond strongly to grating and bar stimuli displayed at a certain phase and visual field location, complex cell responses are insensitive to small translations of the stimulus within the receptive field [Hubel & Wiesel (1962) J. Physiol. (Lond.), 160, 106-154; Kjaer et al. (1997) J. Neurophysiol., 78, 3187-3197]. This constancy in the response to variations of the stimuli is commonly called invariance. Hubel and Wiesel's classical model of the primary visual cortex proposes a connectivity scheme which successfully describes simple and complex cell response properties. However, the question as to how this connectivity arises during normal development is left open. Based on their work and inspired by recent physiological findings we suggest a network model capable of learning from natural stimuli and developing receptive field properties which match those of cortical simple and complex cells. Stimuli are drawn from videos obtained by a camera mounted to a cat's head, so they should approximate the natural input to the cat's visual system. The network uses a competitive scheme to learn simple and complex cell response properties. Employing delayed signals to learn connections between simple and complex cells enables the model to utilize temporal properties of the input. We show that the temporal structure of the input gives rise to the emergence and refinement of complex cell receptive fields, whereas removing temporal continuity prevents this processes. This model lends a physiologically based explanation of the development of complex cell invariance response properties.
机译：初级视觉皮层中的神经元通常分为简单或复杂。简单细胞对在特定阶段和视野位置显示的光栅和条形刺激有强烈反应，而复杂细胞对接收域内刺激的小翻译不敏感[Hubel＆Wiesel（1962）J. Physiol。 （Lond。），160，106-154; Kjaer等。 （1997）J. Neurophysiol。，78，3187-3197]。这种对刺激变化的反应的恒定性通常称为不变性。 Hubel和Wiesel的主要视觉皮层的经典模型提出了一种连接方案，该方案成功地描述了简单和复杂的细胞反应特性。但是，关于这种连通性在正常开发过程中如何产生的问题尚待解决。基于他们的工作并受到近期生理学发现的启发，我们提出了一种网络模型，该模型能够从自然刺激中学习并发展出与皮层简单和复杂细胞相匹配的感受野特性。刺激来自安装在猫头上的摄像机获得的视频，因此它们应近似于猫视觉系统的自然输入。该网络使用竞争方案来学习简单和复杂的小区响应属性。利用延迟信号来学习简单单元与复杂单元之间的联系，可使模型利用输入的时间特性。我们表明，输入的时间结构引起了复杂细胞接受域的出现和完善，而消除时间连续性阻止了这一过程。该模型为复杂细胞不变性反应特性的发展提供了基于生理学的解释。

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