自适应压缩传感--神经科学中的一类新的自组织编码模型
Adaptive compressed sensing - a new class of self-organizing coding models for neuroscience
摘要：
稀疏编码网络利用无监督学习来最大化编码效率，已经成功地再现了初级视觉皮层\cite{AN:OlshausenField96}中发现的响应特性。然而，传统的稀疏编码模型要求编码电路能够以一对一的方式对感觉数据进行完全采样，这一要求不被来自丘脑-皮层投影的实验数据所支持。为了减轻这些严格的布线要求，我们提出了一个稀疏编码网络，通过引入压缩感知的框架中的突触学习。我们证明，新的模型进化生物现实的空间平滑的感受野，尽管事实上，前馈连接subsamples输入，从而学习依赖于贫困和扭曲的帐户的原始视觉数据。此外，我们证明，该模型可以形成一个一般的计划皮质通信：它可以形成有意义的表示在一个次要的感觉区，它接收输入从主要的感觉区通过“压缩”皮质-皮质投影。最后，我们证明了我们的模型属于一类新的稀疏编码算法，其中经常性的连接是必不可少的形成空间感受野。
英文摘要：
Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex \cite{AN:OlshausenField96}. However, conventional sparse coding models require that the coding circuit can fully sample the sensory data in a one-to-one fashion, a requirement not supported by experimental data from the thalamo-cortical projection. To relieve these strict wiring requirements, we propose a sparse coding network constructed by introducing synaptic learning in the framework of compressed sensing. We demonstrate that the new model evolves biologically realistic spatially smooth receptive fields despite the fact that the feedforward connectivity subsamples the input and thus the learning has to rely on an impoverished and distorted account of the original visual data. Further, we demonstrate that the model could form a general scheme of cortical communication: it can form meaningful representations in a secondary sensory area, which receives input from the primary sensory area through a "compressing" cortico-cortical projection. Finally, we prove that our model belongs to a new class of sparse coding algorithms in which recurrent connections are essential in forming the spatial receptive fields.
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