Spontaneous activity and neural decoding in the developing brain. This project aims to investigate how patterns of neural activity emerge in the developing brain, using the zebrafish as a model system. This project expects to generate new knowledge regarding the functional significance of spontaneously generated activity, and how it interacts with sensory experience. The expected outcomes of this project include enhanced capacity at the interface between neuroscience and computation. This should ....Spontaneous activity and neural decoding in the developing brain. This project aims to investigate how patterns of neural activity emerge in the developing brain, using the zebrafish as a model system. This project expects to generate new knowledge regarding the functional significance of spontaneously generated activity, and how it interacts with sensory experience. The expected outcomes of this project include enhanced capacity at the interface between neuroscience and computation. This should provide significant benefits including greater insight into normal brain development, and the formulation of new concepts potentially relevant for brain-inspired computing.Read moreRead less
How does environmental enrichment affect brain development? This project aims to use brain imaging and advanced computational analyses to investigate how early sensory experience affects brain development. It adopts the larval zebrafish as a model system, since they display sophisticated behaviours from an early age, and neural activity can be recorded at whole-brain scale with single neuron resolution. The project aims to generate new knowledge regarding environmental effects on brain developme ....How does environmental enrichment affect brain development? This project aims to use brain imaging and advanced computational analyses to investigate how early sensory experience affects brain development. It adopts the larval zebrafish as a model system, since they display sophisticated behaviours from an early age, and neural activity can be recorded at whole-brain scale with single neuron resolution. The project aims to generate new knowledge regarding environmental effects on brain development and behaviour. This will provide significant benefits including greater insight into normal brain development, and the formulation of new concepts potentially relevant for brain-inspired computing. The expected outcomes also include enhanced capacity at the interface between neuroscience and computation.Read moreRead less
How do patterns of brain activity emerge during early life? This project uses theory and experiment to investigate how neural coding emerges in the developing brain. It adopts the larval zebrafish as a model system, because neural activity can be recorded at whole-brain scale but with single neuron resolution. The project expects to generate new knowledge regarding how neural activity comes to represent sensory stimuli, and new statistical models for interpreting large-scale patterns of neural a ....How do patterns of brain activity emerge during early life? This project uses theory and experiment to investigate how neural coding emerges in the developing brain. It adopts the larval zebrafish as a model system, because neural activity can be recorded at whole-brain scale but with single neuron resolution. The project expects to generate new knowledge regarding how neural activity comes to represent sensory stimuli, and new statistical models for interpreting large-scale patterns of neural activity. This will provide significant benefits including greater insight into normal brain development, and the formulation of new concepts potentially relevant for brain-inspired computing. The expected outcomes also include enhanced capacity at the interface between neuroscience and computation.Read moreRead less
A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algor ....A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algorithm-based search obtain the optimal number of layers, clusters and base classifiers. The expected outcomes of the proposed framework are advances in classifier learning. The final outcome may be novel methods which will bring in diversity during the learning of the base classifiers and provide an optimal ensemble classifier for real-world applications.Read moreRead less
Mechanisms of nerve fibre guidance by molecular gradients. Brain wiring is crucial for brain function. The project will investigate the basic principles underlying the development of brain wiring, using both experiments and mathematical models. This will lead a predictive model of how wiring develops, both in normal and abnormal situations.
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Meshless, numerical modelling for polymer processing. The new modelling technology will significantly improve Australian polymer producers' competitiveness and their ability to respond to international market forces. The technology will lead to new opportunities for Australian companies that develop simulation software. Our consumers will benefit from improvements in the design of polymer products. Our researchers in rheology and computational mechanics will gain further opportunities to extend ....Meshless, numerical modelling for polymer processing. The new modelling technology will significantly improve Australian polymer producers' competitiveness and their ability to respond to international market forces. The technology will lead to new opportunities for Australian companies that develop simulation software. Our consumers will benefit from improvements in the design of polymer products. Our researchers in rheology and computational mechanics will gain further opportunities to extend the advances this project will make.Read moreRead less
Computational Modeling of RNA Control Networks. One of the most exciting new ideas for understanding the regulation of gene expression involves the contribution of intronic and other non-protein coding RNAs to regulatory networks within cells. This novel role for intronic RNA is currently making headlines within the molecular biology community but has not yet been modelled computationally. The network of genetic regulatory interactions forms a complex system which is amenable to computational ....Computational Modeling of RNA Control Networks. One of the most exciting new ideas for understanding the regulation of gene expression involves the contribution of intronic and other non-protein coding RNAs to regulatory networks within cells. This novel role for intronic RNA is currently making headlines within the molecular biology community but has not yet been modelled computationally. The network of genetic regulatory interactions forms a complex system which is amenable to computational analysis. This project aims to extend current models to incorporate intronic RNA feedback control, complementing parallel studies in vivo, and computationally testing ideas essential to the theoretical understanding of the basis of life.Read moreRead less
Learning for Teaching in Disadvantaged Schools. This project focuses on what and how primary school teachers learn about improving classroom practices from co-inquiry interventions. The effective diagnosis of student learning difficulties and the design of educational interventions based on such diagnosis is a core component of quality teaching. Yet many teachers have not acquired the knowledge and skills to undertake such learning diagnostic and design work. The project plans to engage practiti ....Learning for Teaching in Disadvantaged Schools. This project focuses on what and how primary school teachers learn about improving classroom practices from co-inquiry interventions. The effective diagnosis of student learning difficulties and the design of educational interventions based on such diagnosis is a core component of quality teaching. Yet many teachers have not acquired the knowledge and skills to undertake such learning diagnostic and design work. The project plans to engage practitioners in co-inquiry through collaborative analysis of professional learning conversations and classroom practices across disadvantaged public schools in urban and regional locations across Queensland. It aims to examine the sustainability of co-inquiry models to improve student learning.Read moreRead less