Please use this identifier to cite or link to this item: http://theses.ncl.ac.uk/jspui/handle/10443/2820
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLuo, Junwen-
dc.date.accessioned2016-01-15T14:06:49Z-
dc.date.available2016-01-15T14:06:49Z-
dc.date.issued2015-
dc.identifier.urihttp://hdl.handle.net/10443/2820-
dc.descriptionPhD Thesisen_US
dc.description.abstractThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work.en_US
dc.language.isoenen_US
dc.publisherNewcastle Universityen_US
dc.titleDigital neural circuits : from ions to networksen_US
dc.typeThesisen_US
Appears in Collections:School of Electrical and Electronic Engineering

Files in This Item:
File Description SizeFormat 
Luo, J. 2015.pdfThesis9.15 MBAdobe PDFView/Open
dspacelicence.pdfLicence43.82 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.