论文标题
建模视网膜神经网络的演变
Modeling the Evolution of Retina Neural Network
论文作者
论文摘要
对于初级视觉处理至关重要的是，视网膜电路显示了许多相似的结构，这些结构在非常广泛的物种中，脊椎动物和非脊椎动物，尤其是功能性成分，例如横向抑制作用。这种令人惊讶的保守模式提出了一个问题，即进化如何导致它，以及是否有其他选择也可以促使有用的预处理。在这里，我们设计了一种使用遗传算法的方法，该算法具有许多自由度，可导致其功能与生物视网膜相似的体系结构，以及在结构和功能上不同的有效替代方法。我们将该模型与自然进化进行了比较，并讨论了我们的框架如何进入机器学习中神经网络模型的目标驱动搜索和可持续增强。
Vital to primary visual processing, retinal circuitry shows many similar structures across a very broad array of species, both vertebrate and non-vertebrate, especially functional components such as lateral inhibition. This surprisingly conservative pattern raises a question of how evolution leads to it, and whether there is any alternative that can also prompt helpful preprocessing. Here we design a method using genetic algorithm that, with many degrees of freedom, leads to architectures whose functions are similar to biological retina, as well as effective alternatives that are different in structures and functions. We compare this model to natural evolution and discuss how our framework can come into goal-driven search and sustainable enhancement of neural network models in machine learning.
