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ELM solutions for event-based systems
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DOI：10.1016/j.neucom.2014.01.074.png)
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摘要
En 中文
Whilst most engineered systems use signals that are continuous in time, there is a domain of systems in which signals consist of events. Events, like Dirac delta functions, have no meaningful time duration. Many important real-world systems are intrinsically event-based, including the mammalian brain, in which the primary packets of data are spike events, or action potentials. In this domain, signal processing requires responses to spatio-temporal patterns of events. We show that some straightforward modifications to the standard ELM topology produce networks that are able to perform spatiotemporal event processing online with a high degree of accuracy. The modifications involve the re-definition of hidden layer units as synaptic kernels, in which the input delta functions are transformed into continuous-valued signals using a variety of impulse-response functions. This permits the use of linear solution methods in the output layer, which can produce events as output, if modeled as a classifier - the output classes are event or no event. We illustrate the method in application to a spike-processing problem. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Extreme learning machine
Event based processing
Synaptic kernel
Mixed domain processing
Spatio-temporal pattern recognition
Spatio-temporal receptive field
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6.5
论文数:
2.5W
被引数:
6.5W
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