Science. 1995 Sep 29;269(5232):1860-3. doi: 10.1126/science.269.5232.1860.
Replicator neural networks self-organize by using their inputs as desired outputs; they internally form a compressed representation for the input data. A theorem shows that a class of replicator networks can, through the minimization of mean squared reconstruction error (for instance, by training on raw data examples), carry out optimal data compression for arbitrary data vector sources. Data manifolds, a new general model of data sources, are then introduced and a second theorem shows that, in a practically important limiting case, optimal-compression replicator networks operate by creating an essentially unique natural coordinate system for the manifold.
复制者神经网络通过将输入用作期望输出来自组织；它们内部为输入数据形成一个压缩表示。一个定理表明，一类复制者网络可以通过最小化均方重建误差（例如，通过对原始数据示例进行训练），为任意数据向量源执行最佳数据压缩。然后引入了数据流形，这是一种新的数据源通用模型，第二个定理表明，在一个实际重要的极限情况下，最优压缩复制者网络通过为流形创建一个本质上唯一的自然坐标系来运行。
