Toggle Main Menu Toggle Search

Open Access padlockePrints

Digital neural circuits : from ions to networks

Lookup NU author(s): Dr Jun Luo


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


he 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.

Publication metadata

Author(s): Luo JW

Publication type: Online Publication

Publication status: Published

Series Title: School of Electrical and Electronic Engineering

Year: 2015

Access Year: 2016

Description: PhD Thesis

Acceptance date: 07/12/2015

Publisher: Newcastle University

Place Published: Newcastle upon Tyne

Access Date: 16 November

Type of Medium: PhD Thesis



Link to this publication