Li Lei, Zou Tingting, Cai Tao, Niu Dejiao, Zhu Yuquan
Department of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China.
Comput Intell Neurosci. 2021 Mar 17;2021:6680833. doi: 10.1155/2021/6680833. eCollection 2021.
As a new type of artificial neural network model, HTM has become the focus of current research and application. The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training time overhead and may cause the spatial pool to become unstable. To overcome these disadvantages, we propose a fast spatial pool learning algorithm of HTM based on minicolumn's nomination, where the minicolumns are selected according to the load-carrying capacity and the synapses are adjusted using compressed encoding. We have implemented the prototype of the algorithm and carried out experiments on three datasets. It is verified that the training time overhead of the proposed algorithm is almost unaffected by the encoding length, and the spatial pool becomes stable after fewer iterations of training. Moreover, the training of the new input does not affect the already trained results.
作为一种新型的人工神经网络模型，HTM已成为当前研究和应用的焦点。稀疏分布式表示是HTM模型的基础，但现有的空间池学习算法训练时间开销大，可能导致空间池不稳定。为克服这些缺点，我们提出了一种基于微柱提名的HTM快速空间池学习算法，根据承载能力选择微柱，并使用压缩编码调整突触。我们实现了该算法的原型，并在三个数据集上进行了实验。验证了所提算法的训练时间开销几乎不受编码长度的影响，经过较少的训练迭代后空间池变得稳定。此外，新输入的训练不影响已训练的结果。
