Learning the nonlinearity of neurons from natural visual stimuli
Learning the nonlinearity of neurons from natural visual stimuli
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DOI：
10.1162/08997660360675026
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发表时间：
2003-08-01
影响因子：
2.9
通讯作者：
König, P
中科院分区：
文献类型：
--
作者：
Kayser, C;Körding, KP;König, P
神经网络中的学习通常应用于与线性核相关的参数，并保持模型的非线性固定。因此，对于成功的模型，非线性的性质和参数必须使用通常缺失的先验知识来指定。在这里，我们研究了非线性与线性核同时适应。我们使用自然的视觉刺激来训练一个简单的视觉系统模型。许多神经元汇聚成一个能量检测器，与现有的复杂细胞模型相匹配。描述非线性的参数的总体分布与最近的生理结果很吻合。随机洗牌自然刺激和粉红噪声的控制表明，仿真和实验结果的匹配取决于自然刺激的高阶统计特性。
Learning in neural networks is usually applied to parameters related to linear kernels and keeps the nonlinearity of the model fixed. Thus, for successful models, properties and parameters of the nonlinearity have to be specified using a priori knowledge, which often is missing. Here, we investigate adapting the nonlinearity simultaneously with the linear kernel. We use natural visual stimuli for training a simple model of the visual system. Many of the neurons converge to an energy detector matching existing models of complex cells. The overall distribution of the parameter describing the nonlinearity well matches recent physiological results. Controls with randomly shuffled natural stimuli and pink noise demonstrate that the match of simulation and experimental results depends on the higher-order statistical properties of natural stimuli.