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Constructing robust liquid state machines to process highly variable data streams


Schliebs, S; Fiasché, M; Kasabov, N (2012). Constructing robust liquid state machines to process highly variable data streams. Artificial Neural Networks and Machine Learning (ICANN 2012) , Springer(7552):604-611.

Abstract

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2012
Deposited On:07 Mar 2013 08:26
Last Modified:05 Apr 2016 16:36
Publisher:Lecture Notes in Computer Science
Series Name: Artificial Neural Networks and Machine Learning (ICANN 2012)
Number of Pages:8
Publisher DOI:https://doi.org/10.1007/978-3-642-33269-2_76

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