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Synaptic plasticity, an emergent property of synaptic networks, has shown strong correlation to one of the essential functions of the brain, memory formation. Through understanding synaptic plasticity, we hope to discover the modulators and mechanisms that trigger memory formation. In this paper, we first review the well understood modulators and mechanisms(More)
Alzheimer's disease (AD) is a devastating, incurable neurodegenerative disease affecting millions of people worldwide. Dysregulation of intracellular Ca(2+) signaling has been observed as an early event prior to the presence of clinical symptoms of AD and is believed to be a crucial factor contributing to its pathogenesis. The progressive and sustaining(More)
Microarrays technology has been expanding remarkably since its launch about 15 years ago. With its advancement along with the increase of popularity, the technology affords the luxury that gene expressions can be measured in any of its multiple platforms. However, the generated results from the microarray platforms remain incomparable. In this direction, we(More)
This Paper investigates the use of digital image analysis techniques for developing an automated kiwifruit counting system. Three simple counting methods followed by a minimum distance classifier based segmentation technique in L*a*b colour space were studied. Images were taken prior to harvesting at a New Zealand kiwifruit orchard. Accurate counting of(More)
An application of model-based reasoning and model-based learning to an operative diagnostic domain such as electrical power transmission networks is presented. Most of the research in model-based diagnosis is based on maintenance diagnosis. Operative diagnosis, on the other hand, is done while the system is still in operation even after the fault. We plan(More)
Lowering the threshold of cellular senescence, the process employed by cells to thwart abnormal cell proliferation, though inhibition of CDK2 or Skp2 (regulator of CDK inhibitors) has been recently suggested as a potential avenue for cancer treatment. In this study, we employ a published mathematical model of G1/S transition involving the DNA-damage signal(More)
In this paper, prediction capability of a hybrid Artificial Neural Networks (ANN) was investigated to solve the groundwater inverse problem. Initially, a Multi Layer Perceptron (MLP) network was developed and it was found that network produced better results when the target range of the parameters is smaller. Therefore, a Self-Organising Network (SON) was(More)
Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary(More)
EXTENDED ABSTRACT Neural Networks have the capability to approximate nonlinear functions to a high degree of accuracy owing to its nonlinear processing in the hidden layer neurons. However, the optimum network structure that is required for solving a particular problem is still an active area of research. In the past, several network pruning methods based(More)