Computational models provide additional tools for studying the brain,however,many techniques are currently disconnected from each other.There is a need for new computational approaches that span the range of physics o...Computational models provide additional tools for studying the brain,however,many techniques are currently disconnected from each other.There is a need for new computational approaches that span the range of physics operating in the brain.In this review paper,we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre.The embedded element method is a mesh superposition technique used within finite element analysis.This method allows for the incorporation of axonal fiber tracts to be explicitly represented.Here,we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury.We explore the potential application of the embedded element method in areas of electrophysiology,neurodegeneration,neuropharmacology and mechanobiology.We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.展开更多
Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into accou...Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.展开更多
This article describes the importance of detecting grain deviation caused by knots and reviews the main methods used in measuring grain orientation surrounding knots. It discusses the potential of using Diffusion Tens...This article describes the importance of detecting grain deviation caused by knots and reviews the main methods used in measuring grain orientation surrounding knots. It discusses the potential of using Diffusion Tensor Magnetic Resonance Imaging to track and map the grain deviation caused by knots.展开更多
基金support provided by Computational Fluid Dynamics Research Corporation(CFDRC)under a sub-contract funded by the Department of Defense,Department of Health Program through contract W81XWH-14-C-0045
文摘Computational models provide additional tools for studying the brain,however,many techniques are currently disconnected from each other.There is a need for new computational approaches that span the range of physics operating in the brain.In this review paper,we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre.The embedded element method is a mesh superposition technique used within finite element analysis.This method allows for the incorporation of axonal fiber tracts to be explicitly represented.Here,we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury.We explore the potential application of the embedded element method in areas of electrophysiology,neurodegeneration,neuropharmacology and mechanobiology.We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.
基金Project supported by the National Natural Science Foundation of China(Grant No.61332003)High Performance Computing Laboratory,China(Grant No.201501-02)
文摘Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.
基金The support of the New Zealand Foundation for Research,Science and Technology (Contract No. C04X0705) is gratefully acknowledged
文摘This article describes the importance of detecting grain deviation caused by knots and reviews the main methods used in measuring grain orientation surrounding knots. It discusses the potential of using Diffusion Tensor Magnetic Resonance Imaging to track and map the grain deviation caused by knots.
