Short term memory in input-driven linear dynamical systems

被引:17
|
作者
Tino, Peter [1 ]
Rodan, Ali [2 ]
机构
[1] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
[2] Univ Jordan, King Abdulla Sch Informat Technol 2, Amman, Jordan
基金
英国生物技术与生命科学研究理事会;
关键词
Short term memory capacity; Fisher memory curve; Recurrent neural network; Echo state network; Reservoir computing;
D O I
10.1016/j.neucom.2012.12.041
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We investigate the relation between two quantitative measures characterizing short term memory in input driven dynamical systems, namely the short term memory capacity (MC) [3] and the Fisher memory curve (FMC) [2]. We show that even though MC and FMC map the memory structure of the system under investigation from two quite different perspectives, for linear input driven dynamical systems they are in fact closely related. In particular, under some assumptions, the two quantities can be interpreted as squared 'Mahalanobis' norms of images of the input vector under the system's dynamics. We also offer a detailed rigorous analysis of the relation between MC and FMC in cases of symmetric and cyclic dynamic couplings. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:58 / 63
页数:6
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