Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Justin Chang Li.-
dc.description.abstractLiquid State Machine is a relatively new system which is capable of recognising real-world temporal patterns on noisy continuous input streams. We will also investigate on its applicability for practical usage. By first looking at how the human brain model and biological neural network has brought about the development of artificial neural networks. We will also get to see how the spiking neuron model would have an advantage over conventional artificial neural network in classifying the real-world temporal patterns. In this project, we selected a practical input type for implementation which would be visual input, inspired by how human see. The challenges of such an implementation would be in creating a good encoding scheme to change the 2-dimensional input into spike train(s). A possible encoding scheme has been created in this project which has been successful in classifying the input. The implementation was created in the Matlab environment.en_US
dc.format.extent46 p.en_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleNeural computation : theory and practice behind brainen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorQuah Tong Sengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.organizationA*STAR Institute for Infocomm Researchen_US
dc.contributor.supervisor2Tang Huajinen_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
  Restricted Access
692.11 kBAdobe PDFView/Open

Page view(s)

Updated on Nov 28, 2020

Download(s) 10

Updated on Nov 28, 2020

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.