A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented

Computational models of intracellular signalling in cerebellar Purkinje cells

In spite of the regular and well-characterised anatomy of the cerebellum, its function is still not clear. To understand the function of the cerebellum, it is necessary to understand the behaviour of a single cerebellar Purkinje cell. The behaviour of Purkinje cells is determined by their intracellular calcium dynamics, and by the network of intracellular signalling molecules that control the calcium dynamics. The aim of this thesis is to contribute to an understanding of the intracellular signalling network that is linked to the activation of metabotropic glutamate receptors (mGluRs) in a cerebellar Purkinje cell. In the thesis, ten different computational models of the mGluR signalling network are mathematically analysed and numerically integrated. The main result of this thesis is that the mGluR signalling network can implement an adaptive time delay between the activation of the mGluRs by glutamate and the release of calcium from intracellular stores. The adaptation of the time de..

Differentiating noise and modulators in artificial neural networks

Research in Computational Neural Networks is currently taking place at many different levels; from coarse-grain symbolic models to fine-grain representations of neurons and cell processes. One feature that the different approaches share, is that they are all in relative infancy. Thus, most research concentrates on gross aspects of neural communication and methods of computational simulation. Recently, some clues have been found which point to more subtle mechanisms underlying the information processing capability of neural 'nodes'. These clues are the improvement in network operation by the injection of random noise; and the neurobiological finding that neuropeptides may exist as slower Signal transmission channels between neurons. This study concerns the difference between random noise injection, and directed, low-level, activity injections which are postulated to be produced by neuromodulators such as neuropeptides. The findings of this study are that random noise does, indeed, enhance the operation of coarse-grain neural models; and that a 'neuropeptidergic' analogue also enhances operation; but to a different extent, and probably through a different mechanism. Further testing of a medium-grain computer model gives some indication of how a neuropeptidergic modulation might affect real neurons, by extending the time-course of the activation of the neuron. This appears to be a similar mechanism to that postulated for the coarse-grain 'neuropeptidergic' simulation model. Given these findings, is it possible that signal transmission in real nervous systems assume these mechanisms? If so, it may be possible that a process of concurrent propagation, through different signal channels, also occurs in real nervous systems, making the nervous system much more complex than current models allow

Theory and applications of artificial neural networks

In this thesis some fundamental theoretical problems about artificial neural networks and their application in communication and control systems are discussed. We consider the convergence properties of the Back-Propagation algorithm which is widely used for training of artificial neural networks, and two stepsize variation techniques are proposed to accelerate convergence. Simulation results demonstrate significant improvement over conventional Back-Propagation algorithms. We also discuss the relationship between generalization performance of artificial neural networks and their structure and representation strategy. It is shown that the structure of the network which represent a priori knowledge of the environment has a strong influence on generalization performance. A Theorem about the number of hidden units and the capacity of self-association MLP (Multi-Layer Perceptron) type network is also given in the thesis. In the application part of the thesis, we discuss the feasibility of using artificial neural networks for nonlinear system identification. Some advantages and disadvantages of this approach are analyzed. The thesis continues with a study of artificial neural networks applied to communication channel equalization and the problem of call access control in broadband ATM (Asynchronous Transfer Mode) communication networks. A final chapter provides overall conclusions and suggestions for further work

Simulations of Neocortical Columnar Oscillations

The intentionof this thesis is to examine the role of the neocortical local drcuit in supporting synchronisatLon and fast (gamma) oscUlation. The aim is to include stereotypical features of the local neocortex in model simulations of cortical activity. Modelling is hmitedby scale in number and detail. Model features include three neurontypes(RS, FS and IB) andsynapses with three time courses takenfrom reportedtri-phasic PSPs (fEPSP, flPSP andsIPSP). Cell types and synapses are distributedin a two layer model. The contribution of the layers to columnactivity is investigated. The upperlayer has a tendancy towardspredse synchronisation and can dominate the activity producing synchronisation and oscillation in the whole column. This is attributed to the stronger inhibitory circuit in the upperlayer. The lower layer achieves a less precise synchronisatiorv this is attributed to a lower level of inhibition and the intraburst duration of IB neurons. The significance of this difference in the temporal properties of the two layers is discussed in relation to existing theories andmodels of local cortical function. Following a further consideration of local cortical physiology a new model of cortical functioning is proposed. The key features of this model include: the generation of local oscillations in a vertical interlarninar reciprocal circuit; the apical dendrite providing a sharp coincidence detection functionbetweenthe layers; slow axonal lateral propagationprovidinga time delay network; apical dendrites of bursting cells (CH and IB) providing coincidence detectionbetweenmputs 6:0m distant areas (layer 1 inputs) and local activity; bursting cell innervationof intemeurons, linking the local oscillation cy de to coinddence detection. This moddis termed an'intrinsically osdllating time coding networld (lOTCN). Specific predictions are made concerning the functiorungof the local circuit m neocortex, and the connectivity of CH neurons

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u