Orzó L
Department of Anatomy, Semmelweis University Medical School, Budapest, Hungary.
Acta Biochim Biophys Hung. 1991;26(1-4):127-30.
The greatest practical limitation of the associative memory models, especially the Hopfield model is the low storage capacity. It has been shown by Gardner, that the Hopfield type models storage limit is 2*N, where N is the number of the processing elements or neurons. For biased patterns, on the other hand, it is much greater. But in general the input patterns are not biased. To approach to this problem and to increase the storage capacity of the model, the input patterns have to be diluted by some conversion method particularly which uses convergence and divergence in neuroanatomical sense. Based on this model these parameters can be estimated. As a consequence of this bias and the divergence, the storage capacity is increased. This preprocessing method doesn't lead to the loss of information and keeps the error correcting ability of the model.
关联记忆模型，尤其是霍普菲尔德模型，最大的实际局限性在于存储容量较低。加德纳已经证明，霍普菲尔德型模型的存储极限是2*N，其中N是处理元件或神经元的数量。另一方面，对于有偏模式，存储极限要大得多。但一般来说，输入模式是无偏的。为了解决这个问题并提高模型的存储容量，必须通过某种转换方法对输入模式进行稀释，特别是那种在神经解剖学意义上使用收敛和发散的方法。基于这个模型，可以估计这些参数。由于这种偏差和发散，存储容量得以增加。这种预处理方法不会导致信息丢失，并保留了模型的纠错能力。
