Fuzzification of spiked neural networks

Reid, David; Muyeba, Maybin

Abstract

Biological systems are slow, wide and messy whereas computer systems are fast, deep and precise. Fuzzy neural networks use fuzzy logic to implement higher level reasoning and incorporate expert knowledge into the system while neural networks deal with the low level computational structures capable of learning and adaptation. Whereas the first 2 generations of neural network are ldquorate encodedrdquo, spike neural networks (SNNs) are a relatively new type and potentially very powerful neural network (so called 3rd generation of neural network) that uses temporal encoding of information in a much more biologically realistic way than previous generations. This paper demonstrates how fuzzification of SNNs (FSNNs) may take place using interval type-2 fuzzy sets (IT2FS).

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: Second UKSIM European Symposium on Computer Modeling and Simulation
• Start Date: Sep 8, 2008
• Publication Date: 2008
• Deposit Date: Apr 11, 2025
• Journal: Proceedings - EMS 2008, European Modelling Symposium, 2nd UKSim European Symposium on Computer Modelling and Simulation
• Peer Reviewed: Peer Reviewed
• ISBN: 978-0-7695-3325-4
• DOI: https://doi.org/10.1109/EMS.2008.108