Sparse coding algorithms trained on natural images can accurately predict the
features that excite visual cortical neurons, but it is not known whether such
codes can be learned using biologically realistic plasticity rules. We have
developed a biophysically motivated spiking network, relying solely on
synaptically local information, that can predict the full diversity of V1
simple cell receptive field shapes when trained on natural images. This
represents the first demonstration that sparse coding principles, operating
within the constraints imposed by cortical architecture, can successfully
reproduce these receptive fields. We further prove, mathematically, that
sparseness and decorrelation are the key ingredients that allow for
synaptically local plasticity rules to optimize a cooperative, linear
generative image model formed by the neural representation. Finally, we discuss
several interesting emergent properties of our network, with the intent of
bridging the gap between theoretical and experimental studies of visual cortex.

训练在自然图像上的稀疏编码算法能够准确预测视觉皮层神经元所激发的特征，但不确定是否可以使用生物学真实的可塑性规则来学习这些代码。我们开发了一种生物物理模型的尖峰网络，仅依靠突触局部信息就能预测自然图像上 V1 简单细胞感受野的全部多样性形状，这是首次证明，稀疏编码原则在皮层结构限制下操作时可以成功地再现这些感受野。我们进一步证明，稀疏性和去相关是允许突触局部可塑性规则优化神经表达形成的合作线性生成图像模型的关键因素。最后，我们讨论了我们的网络的几个有趣的新兴属性，以期弥合视觉皮层的理论和实验研究之间的差距。