Neural Networks as Cybernetic Systems - Part I

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology.

Neural Networks as Cybernetic Systems - Part II

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology.

Neural Networks as Cybernetic Systems (3rd and revised edition) - Part I

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology. This third edition essentially compares with the 2nd one, but has been improved by correction of errors and by a rearrangement and minor expansion of the sections referring to recurrent networks. These changes hopefully allow for an easier comprehension of the essential aspects of this important domain that has received growing attention during the last years.

Neural Networks as Cybernetic Systems (3rd and revised edition) - Part 2

eural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology. This third edition essentially compares with the 2nd one, but has been improved by correction of errors and by a rearrangement and minor expansion of the sections referring to recurrent networks. These changes hopefully allow for an easier comprehension of the essential aspects of this important domain that has received growing attention during the last years.

XML for Model Specification in Neuroscience

One of the main roles of the Neural Open Markup Language, NeuroML, is to facilitate cooperation in building, simulating, testing and publishing models of channels, neurons and networks of neurons. MorphML, which was developed as a common format for exchange of neural morphology data, is distributed as part of NeuroML but can be used as a stand-alone application. In this collection of tutorials and workshop summary, we provide an overview of these XML schemas and provide examples of their use in down-stream applications. We also summarize plans for the further development of XML specifications for modeling channels, channel distributions, and network connectivity.

SNNAP: A Tool for Teaching Neuroscience

Simulation tools aid in learning neuroscience by providing the student with an interactive environment to carry out simulated experiments and test hypotheses. The field of neuroscience is well suited for the use of simulation tools since nerve cell signaling can be described by mathematical equations and solved by computer. Neural signaling entails the propagation of electrical current along nerve membrane and transmission to neighboring neurons through synaptic connections. Action potentials and synaptic transmission can be simulated and results displayed for visualization and analysis. The neurosimulator SNNAP (Simulator for Neural Networks and Action Potentials) is a simulation environment that provides users with editors for model building, simulator engine and visual display editor. This paper presents several modeling examples that illustrate some of the capabilities and features of SNNAP. First, the Hodgkin-Huxley (HH) model is presented and the threshold phenomenon is illustrated. Second, small neural networks are described with HH models using various synaptic connections available with SNNAP. Synaptic connections may be modulated through facilitation or depression with SNNAP. A study of vesicle pool dynamics is presented using an AMPA receptor model. Finally, a central pattern generator model of the Aplysia feeding circuit is illustrated as an example of a complex network that may be studied with SNNAP. Simulation code is provided for each case study described and tasks are suggested for further investigation.

The MESH book : landscape / infrastructure

Resulting from a series of student-run 'Edge' conferences that have been held in Australia and New Zealand (beginning at RMIT in 1983), The Mesh Book is a collection of essays grouped into themes of Invisible Infrastructures (systems of belief), Immanent Infrastructures (natural systems) and Present Infrastructures (roads and services). Ranging from esoteric discussions to analytical case studies, the book assembles a broad spectrum of ideas on the landscape within the context of Australia and a contemporary study of place.

Gateways placement in backbone wireless mesh networks

This paper presents a novel algorithm for the gateway placement problem in Backbone Wireless Mesh Networks (BWMNs). Different from existing algorithms, the new algorithm incrementally identifies gateways and assigns mesh routers to identified gateways. The new algorithm can guarantee to find a feasible gateway placement satisfying Quality-of-Service (QoS) constraints, including delay constraint, relay load constraint and gateway capacity constraint. Experimental results show that its performance is as good as that of the best of existing algorithms for the gateway placement problem. But, the new algorithm can be used for BWMNs that do not form one connected component, and it is easy to implement and use.

A point interpolation method with locally smoothed strain field (PIM-LS2) for mechanics problems using triangular mesh

A point interpolation method with locally smoothed strain field (PIM-LS2) is developed for mechanics problems using a triangular background mesh. In the PIM-LS2, the strain within each sub-cell of a nodal domain is assumed to be the average strain over the adjacent sub-cells of the neighboring element sharing the same field node. We prove theoretically that the energy norm of the smoothed strain field in PIM-LS2 is equivalent to that of the compatible strain field, and then prove that the solution of the PIM- LS2 converges to the exact solution of the original strong form. Furthermore, the softening effects of PIM-LS2 to system and the effects of the number of sub-cells that participated in the smoothing operation on the convergence of PIM-LS2 are investigated. Intensive numerical studies verify the convergence, softening effects and bound properties of the PIM-LS2, and show that the very ‘‘tight’’ lower and upper bound solutions can be obtained using PIM-LS2.

Biomimetic tubular nanofiber mesh and platelet rich plasma-mediated delivery of BMP-7 for large bone defect regeneration.

There is a growing need for successful bone tissue engineering strategies and advanced biomaterials that mimic the structure and function of native tissues carry great promise. Successful bone repair approaches may include an osteoconductive scaffold, osteoinductive growth factors, cells with an osteogenic potential and capacity for graft vascularisation. To increase osteoinductivity of biomaterials, the local combination and delivery of growth factors has been developed. In the present study we investigated the osteogenic effects of calcium phosphate (CaP)-coated nanofiber mesh tube-mediated delivery of BMP-7 from a PRP matrix for the regeneration of critical sized segmental bone defects in a small animal model. Bilateral full-thickness diaphyseal segmental defects were created in twelve male Lewis rats and nanofiber mesh tubes were placed around the defect. Defects received either treatment with a CaP-coated nanofiber mesh tube (n = 6), an un-coated nanofiber mesh tube (n=6) a CaP-coated nanofiber mesh tube with PRP (n=6) or a CaP-coated nanofiber mesh tube in combination with 5 μg BMP-7 and PRP (n = 6). After 12 weeks, bone volume and biomechanical properties were evaluated using radiography, microCT, biomechanical testing and histology. The results demonstrated significantly higher biomechanical properties and bone volume for the BMP group compared to the control groups. These results were supported by the histological evaluations, where BMP group showed the highest rate of bone regeneration within the defect. In conclusion, BMP-7 delivery via PRP enhanced functional bone defect regeneration, and together these data support the use of BMP-7 in the treatment of critical sized defects.

Adaptive finite element analysis of elliptic problems based on bubble-type local mesh generation

A new mesh adaptivity algorithm that combines a posteriori error estimation with bubble-type local mesh generation (BLMG) strategy for elliptic differential equations is proposed. The size function used in the BLMG is defined on each vertex during the adaptive process based on the obtained error estimator. In order to avoid the excessive coarsening and refining in each iterative step, two factor thresholds are introduced in the size function. The advantages of the BLMG-based adaptive finite element method, compared with other known methods, are given as follows: the refining and coarsening are obtained fluently in the same framework; the local a posteriori error estimation is easy to implement through the adjacency list of the BLMG method; at all levels of refinement, the updated triangles remain very well shaped, even if the mesh size at any particular refinement level varies by several orders of magnitude. Several numerical examples with singularities for the elliptic problems, where the explicit error estimators are used, verify the efficiency of the algorithm. The analysis for the parameters introduced in the size function shows that the algorithm has good flexibility.

Specialist nursing training in Poland: Applications for neuroscience nursing

Background: Nurses have a pivotal role in providing, facilitating, advocating and promoting the best possible care and outcome for the client. To ensure decisions and actions are based on current standards of practice, nurses must be accountable for participation in ongoing education in their area of practice. Aim: To present a description of the current state of Polish nursing education and specialized model for neurological and neurosurgical nursing that can be utilized for both undergraduate and postgraduate continuing education in Poland. Data sources: The model of postgraduate training introduced in Poland in 2000 was taken into consideration in developing the framework for neuroscience nursing postgraduate continuing education presented here. The framework for neurological continuing education is also based on a review of the literature and is consistent with Poland’s legally binding professional nursing regulations (normative and implementing regulations). Conclusion: The model demonstrates the need for the content of pre- and post-undergraduate degree education in neurological nursing to be graduated, based on the frameworks for undergraduate education (acquiring the knowledge and basic skills for performing the work of nurses) and postgraduate education (acquiring knowledge and specialist skills necessary for providing advanced nursing care including medical acts on patients with nervous system diseases). Implications for nursing: New and advanced skills gained in specialization training can be applied to complex functions, roles and professional tasks undertaken by nurses in relation to care of patients with neurological dysfunctions.