A new computational approach for modeling diffusion tractography in the brain

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.

CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations

Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.

Degree of dopaminergic degeneration measured by ^(99m)Tc-TRODAT-1 SPECT/CT imaging

To prevent and treat Parkinson＇s disease in its early stages,it is essential to be able to detect the degree of early dopaminergic neuron degeneration.Dopamine transporters（DAT） in the striatum regulate synaptic dopamine levels,and striatal ^99mTc-TRODAT-1 single-photon emission computed tomography（-SPECT） imaging is a marker for presynaptic neuronal degeneration.However,the association between the degree of dopaminergic degeneration and in vivo ^99mTc-TRODAT-1 SPECT imaging is unknown.Therefore,this study investigated the association between the degree of 6-hydroxydopamine（6-OHDA）-induced dopaminergic degeneration and DAT imaging using^99mTc-TRODAT-1 SPECT in rats.Different degrees of nigrostriatal dopamine depletion were generated by injecting different doses of 6-OHDA（2,4,and 8 μg） into the right medial forebrain bundle.The degree of nigrostriatal dopaminergic neuron degeneration was assessed by rotational behavior and immunohistochemical staining.The results showed that striatal ^99mTc-TRODAT-1 binding was significantly diminished both in the ipsilateral and the contralateral sides in the 4 and 8 μg 6-OHDA groups,and that DAT ^99mTc-TRODAT-1 binding in the ipsilateral striatum showed a high correlation to apomorphine-induced rotations at 8 weeks post-lesion（r = –0.887,P 〈 0.01）.There were significant correlations between DAT ^99mTc-TRODAT-1 binding in the ipsilateral striatum and the amount of tyrosine hydroxylase immunoreactive neurons in the ipsilateral substantia nigra in the 2,4,and 8 μg 6-OHDA groups at 8 weeks post-lesion（r = 0.899,P 〈 0.01）.These findings indicate that striatal DAT imaging using ^99mTc-TRODAT-1 is a useful technique for evaluating the severity of dopaminergic degeneration.

Current-driven dynamics of skyrmion bubbles in achiral uniaxial magnets

We report dynamics of skyrmion bubbles driven by spin-transfer torque in achiral ferromagnetic nanostripes using micromagnetic simulations.In a three-dimensional uniaxial ferromagnet with a quality factor that is smaller than 1,the skyrmion bubble is forced to stay at the central nanostripe by a repulsive force from the geometry border.The coherent motion of skyrmion bubbles in the nanostripe can be realized by increasing the quality factor to~3.8.Our results should propel the design for future spintronic devices such as artificial neural computing and racetrack memory based on dipolestabilized skyrmion bubbles.

Grading learning for blind source separation

By generalizing the learning rate parameter to a learning rate matrix, this paper proposes a grading learning algorithm for blind source separation. The whole learning process is divided into three stages: initial stage, capturing stage and tracking stage. In different stages, different learning rates are used for each output component, which is determined by its dependency on other output components. It is shown that the grading learning algorithm is equivariant and can keep the separating matrix from becoming singular. Simulations show that the proposed algorithm can achieve faster convergence, better steady-state performance and higher numerical robustness, as compared with the existing algorithms using fixed, time-descending and adaptive learning rates.

基于不确定度采样准则的费时问题优化算法

在实际工程和控制领域中,许多优化问题的性能评价是费时的,由于进化算法在获得最优解之前需要大量的目标函数评价,无法直接应用其求解这类费时问题.引入代理模型以辅助进化算法是求解计算费时优化问题的有效方法,如何采样新个体对其进行真实的目标函数评价是影响代理模型辅助的进化算法寻优性能的重要因素.鉴于此,利用径向基函数神经网络作为代理模型辅助进化算法,提出一种新的不确定度计算方法,同时结合模型估值构造一种新的填充采样准则以自主地选择新的采样点,从而引导算法在评价次数有限的情况下尽可能地找到目标函数值较好的解.所提出算法与近年来针对计算费时问题的优化算法在7个高达100维的基准问题上进行测试比较,实验结果表明所提出算法在相同评价次数下可以获得更好的优化结果.