Abstract:
:We study the expressive power of positive neural networks. The model uses positive connection weights and multiple input neurons. Different behaviors can be expressed by varying the connection weights. We show that in discrete time and in the absence of noise, the class of positive neural networks captures the so-called monotone-regular behaviors, which are based on regular languages. A finer picture emerges if one takes into account the delay by which a monotone-regular behavior is implemented. Each monotone-regular behavior can be implemented by a positive neural network with a delay of one time unit. Some monotone-regular behaviors can be implemented with zero delay. And, interestingly, some simple monotone-regular behaviors cannot be implemented with zero delay.
journal_name
Neural Computjournal_title
Neural computationauthors
Ameloot TJ,Van den Bussche Jdoi
10.1162/NECO_a_00789subject
Has Abstractpub_date
2015-12-01 00:00:00pages
2623-60issue
12eissn
0899-7667issn
1530-888Xpii
10.1162/NECO_a_00789journal_volume
27pub_type
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