Constructing Robust Liquid State Machines to Process Highly Variable Data Streams
Constructing Robust Liquid State Machines to Process Highly Variable Data Streams
复制标题
构建鲁棒的液态状态机来处理高度可变的数据流
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
N. Kasabov
中科院分区：
文献类型：
--
作者：
S. Schliebs;M. Fiasché;N. Kasabov
在本文中，我们提出了一种有效地控制液体状态机(LSM)储存库中的整体神经活动的机制，以实现储存库对弱刺激的高敏感性以及对强输入的过刺激的更好的抵抗。这个想法是采用一种机制，根据神经元的尖峰活动动态改变神经元的放电阈值。我们的实验证明，采用这种神经模型的储集层显著提高了其分离能力。我们还研究了动态和静态突触在这一背景下的作用。所获得的结果对于基于LSM的真实世界应用可能非常有价值，其中输入信号经常是高度可变的，从而导致网络活动太少或太多的问题。
In this paper, we propose a mechanism to effectively control the overall neural activity in the reservoir of a Liquid State Machine (LSM) in order to achieve both a high sensitivity of the reservoir to weak stimuli as well as an improved resistance to over-stimulation for strong inputs. The idea is to employ a mechanism that dynamically changes the firing threshold of a neuron in dependence of its spike activity. We experimentally demonstrate that reservoirs employing this neural model significantly increase their separation capabilities. We also investigate the role of dynamic and static synapses in this context. The obtained results may be very valuable for LSM based real-world application in which the input signal is often highly variable causing problems of either too little or too much network activity.