论文标题
阈值线性关联网络中本地学习规则的效率
Efficiency of local learning rules in threshold-linear associative networks
论文作者
论文摘要
我们为阈值线性单元的关联网络提供了加德纳的存储能力，并表明，通过Hebbian学习，他们可以比二进制网络更接近这种Gardner绑定，甚至超过它。这在很大程度上是通过对检索到的模式的稀疏来实现的，我们分析了活动的理论和经验分布。由于通过反向传播等非本地学习规则达到最佳能力需要缓慢而神经难以置信的训练程序，因此我们的结果表明，一声自组织的Hebbian学习可以同样有效。
We derive the Gardner storage capacity for associative networks of threshold linear units, and show that with Hebbian learning they can operate closer to such Gardner bound than binary networks, and even surpass it. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like backpropagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.
