Optimised network for sparsely coded patterns
Optimised network for sparsely coded patterns
复制标题
针对稀疏编码模式优化的网络
DOI：
10.1088/0305-4470/22/5/018
复制
发表时间：
1989
期刊：
影响因子：
--
通讯作者：
D. Amit
中科院分区：
文献类型：
--
作者：
C. P. Vicente;D. Amit
存储稀疏编码模式的吸引子神经网络的性能已经被证明在从神经状态的-1，+1表示转换到0，1表示时得到了极大的改善。在这里，作者表明，当这种转变被认为是一个特殊的情况下，取决于一个连续的参数的动态变量的转换，参数的值可以选择，以改善网络的性能，甚至进一步为每个值的偏差的模式。
The performance of attractor neural networks storing sparsely coded patterns has been shown to be greatly improved on shifting from the -1, +1 representation of neural states to the 0, 1 representation. Here the authors show that when this shift is considered as a special case of the transformation of the dynamical variables which depends on a continuous parameter, the value of the parameter can be chosen to improve the performance of the network even further for every value of the bias in the patterns.