The human brain is capable of learning, memorizing, and regenerating a panoply of temporal patterns. A neuro-dynamical mechanism called conceptors offers a method for controlling the dynamics of a recurrent neural network by which a variety of temporal patterns can be learned and recalled. However, conceptors are matrices whose size scales quadratically with the number of neurons in the recurrent neural network, hence they quickly become impractical. In the work reported in this thesis, a variation of conceptors is introduced, called diagonal conceptors, which are diagonal matrices, thus reducing the computational cost drastically. It will be shown that diagonal conceptors achieve the same accuracy as conceptors, but are slightly more unstable. This instability can be improved, but requires further research. Nevertheless, diagonal conceptors show to be a promising practical alternative to the standard full matrix conceptors.

中文翻译：

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通过对角线概念控制循环神经网络

人脑能够学习、记忆和再生一整套时间模式。一种称为概念器的神经动力学机制提供了一种控制循环神经网络动力学的方法，通过该方法可以学习和回忆各种时间模式。然而，概念是矩阵，其大小与循环神经网络中的神经元数量成二次方比例，因此它们很快变得不切实际。在本文报道的工作中，引入了概念器的一种变体，称为对角概念器，它们是对角矩阵，从而大大降低了计算成本。将显示对角线概念器达到与概念器相同的精度，但稍微不稳定。这种不稳定性可以改善，但需要进一步研究。尽管如此，