Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.
Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.
复制标题
DOI：
10.1371/journal.pcbi.1005070
复制
发表时间：
2016-09
影响因子：
4.3
通讯作者：
Gerstner W
中科院分区：
文献类型：
--
作者：
Brito CS;Gerstner W
感觉感受野的发育在过去已经被各种模型建模，包括规范模型，如稀疏编码或独立成分分析和自下而上的模型，如尖峰定时依赖可塑性或Bienenstock-Cooper-Munro模型的突触可塑性。在这里，我们表明，上述各种方法都可以统一到一个共同的原则，即非线性赫布学习。当非线性赫布学习应用于自然图像时，感受野形状受到输入统计和预处理的强烈约束，但在神经元模型或突触可塑性规则的不同非线性选择中仅表现出适度的变化。局部感受野的发展既不需要过完备性，也不需要稀疏的网络活动。对听觉模型或V2发展等替代感觉方式的分析得出了相同的结论。在所有的例子中，可以通过将抽象模型重新表述为非线性赫布学习来先验地预测感受野。因此，非线性赫布学习和自然统计可以解释跨模型和感觉模态的感受野形成的许多方面。大脑如何自我组织以发展出精确调谐的神经元，这个问题至少从胡贝尔和维尔德的发现开始就一直困扰着神经科学家。在过去的几十年里，已经提出了各种理论和模型来描述感受野的形成，特别是V1简单细胞，从自然输入。我们通过证明事实上一个单一的原则就足以解释感受野的发展，从而切断了候选解释的丛林。我们的研究结果来自两个主要的见解。首先，我们表明，许多代表性的模型的感官发展实际上是实施一个共同的原则：非线性赫布学习的变化。其次，我们发现，非线性赫布学习是足够的感受野形成通过感官输入。令人惊讶的结果是，我们的研究结果对模型的具体细节是鲁棒的，并且允许对学习的感受野进行鲁棒的预测。因此，非线性赫布学习在两个意义上是普遍的：它适用于理论家开发的许多模型，以及实验神经科学家研究的许多感觉模态。
The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The question of how the brain self-organizes to develop precisely tuned neurons has puzzled neuroscientists at least since the discoveries of Hubel and Wiesel. In the past decades, a variety of theories and models have been proposed to describe receptive field formation, notably V1 simple cells, from natural inputs. We cut through the jungle of candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. Our results follow from two major insights. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. The surprising result is that our findings are robust of specific details of a model, and allows for robust predictions on the learned receptive fields. Nonlinear Hebbian learning is therefore general in two senses: it applies to many models developed by theoreticians, and to many sensory modalities studied by experimental neuroscientists.
登录
查看更多内容
影响因子：
4.3
作者：
Eichhorn J;Sinz F;Bethge M
通讯作者：
Bethge M
影响因子：
5.3
作者：
Fremaux, Nicolas;Sprekeler, Henning;Gerstner, Wulfram
通讯作者：
Gerstner, Wulfram
影响因子：
25
作者：
Clopath, Claudia;Buesing, Lars;Gerstner, Wulfram
通讯作者：
Gerstner, Wulfram
影响因子：
1.8
作者：
Bell, AJ;Sejnowski, TJ
通讯作者：
Sejnowski, TJ
影响因子：
2.9
作者：
Atick, Joseph J.;Redlich, A. Norman
通讯作者：
Redlich, A. Norman