Dendritic Branches of DNM Help to Improve Approximation accuracy

2019 ◽  
Author(s):  
Jiaxin He ◽  
Jingyao Wu ◽  
Guangchi Yuan ◽  
Yuki Todo
Keyword(s):  
Approximation Accuracy ◽  
Improve Approximation ◽  
Dendritic Branches

SCALING BEHAVIOR OF THE DENDRITIC BRANCHES OF THALAMIC NEURONS

Fractals ◽  
1993 ◽  
Vol 01 (02) ◽  
pp. 171-178 ◽  
Author(s):  
KLAUS-D. KNIFFKI ◽  
MATTHIAS PAWLAK ◽  
CHRISTIANE VAHLE-HINZ
Keyword(s):  
Growth Process ◽  
Scaling Law ◽  
Branching Ratio ◽  
Scaling Behavior ◽  
Neuronal Growth ◽  
Thalamic Neurons ◽  
Dendritic Trees ◽  
Ventrobasal Complex ◽  
Dendritic Branches

The morphology of Golgi-impregnated thalamic neurons was investigated quantitatively. In particular, it was sought to test whether the dendritic bifurcations can be described by the scaling law (d0)n=(d1)n+(d2)nwith a single value of the diameter exponent n. Here d0 is the diameter of the parent branch, d1 and d2 are the diameters of the two daughter branches. Neurons from two functionally distinct regions were compared: the somatosensory ventrobasal complex (VB) and its nociceptive ventral periphery (VBvp). It is shown that for the neuronal trees studied in both regions, the scaling law was fulfilled. The diameter exponent n, however, was not a constant. It increased from n=1.76 for the 1st order branches to n=3.92 for the 7th order branches of neurons from both regions. These findings suggest that more than one simple intrinsic rule is involved in the neuronal growth process, and it is assumed that the branching ratio d0/d1 is not required to be encoded genetically. Furthermore, the results support the concept of the dendritic trees having a statistically identical topology in neurons of VB and VBvp and thus may be regarded as integrative modules.

scholarly journals Deprivation-Induced Homeostatic Spine Scaling In Vivo Is Localized to Dendritic Branches that Have Undergone Recent Spine Loss

Neuron ◽  
2017 ◽  
Vol 96 (4) ◽  
pp. 871-882.e5 ◽  
Author(s):  
Samuel J. Barnes ◽  
Eleonora Franzoni ◽  
R. Irene Jacobsen ◽  
Ferenc Erdelyi ◽  
Gabor Szabo ◽  
...  
Keyword(s):  
Dendritic Branches

scholarly journals Quantitative synapse analysis for cell-type specific connectomics

2018 ◽  
Author(s):  
Dika A. Kuljis ◽  
Khaled Zemoura ◽  
Cheryl A. Telmer ◽  
Jiseok Lee ◽  
Eunsol Park ◽  
...  
Keyword(s):  
Large Scale ◽  
Neural Circuit ◽  
Apical Dendrite ◽  
Automated Detection ◽  
Cell Type ◽  
Disease States ◽  
Specific Localization ◽  
Cell Type Specific ◽  
Dendritic Branches ◽  
Circuit Function

AbstractAnatomical methods for determining cell-type specific connectivity are essential to inspire and constrain our understanding of neural circuit function. We developed new genetically-encoded reagents for fluorescence-synapse labeling and connectivity analysis in brain tissue, using a fluorogen-activating protein (FAP)-or YFP-coupled, postsynaptically-localized neuroligin-1 targeting sequence (FAP/YFPpost). Sparse viral expression of FAP/YFPpost with the cell-filling, red fluorophore dTomato (dTom) enabled high-throughput, compartment-specific localization of synapses across diverse neuron types in mouse somatosensory cortex. High-resolution confocal image stacks of virally-transduced neurons were used for 3D reconstructions of postsynaptic cells and automated detection of synaptic puncta. We took advantage of the bright, far-red emission of FAPpost puncta for multichannel fluorescence alignment of dendrites, synapses, and presynaptic neurites to assess subtype-specific inhibitory connectivity onto L2 neocortical pyramidal (Pyr) neurons. Quantitative and compartment-specific comparisons show that PV inputs are the dominant source of inhibition at both the soma and across all dendritic branches examined and were particularly concentrated at the primary apical dendrite, a previously unrecognized compartment of L2 Pyr neurons. Our fluorescence-based synapse labeling reagents will facilitate large-scale and cell-type specific quantitation of changes in synaptic connectivity across development, learning, and disease states.

Effects of early nerve repair on experimental brachial plexus injury in neonatal rats

2017 ◽  
Vol 43 (3) ◽  
pp. 275-281 ◽  
Author(s):  
Gráinne Bourke ◽  
Aleksandra M. McGrath ◽  
Mikael Wiberg ◽  
Lev N. Novikov
Keyword(s):  
Spinal Cord ◽  
Brachial Plexus ◽  
Brachial Plexus Injury ◽  
Neuroprotective Effect ◽  
Nerve Repair ◽  
Neonatal Rats ◽  
Spinal Motoneurons ◽  
Muscle Weight ◽  
Retrograde Degeneration ◽  
Dendritic Branches

Obstetrical brachial plexus injury refers to injury observed at the time of delivery, which may lead to major functional impairment in the upper limb. In this study, the neuroprotective effect of early nerve repair following complete brachial plexus injury in neonatal rats was examined. Brachial plexus injury induced 90% loss of spinal motoneurons and 70% decrease in biceps muscle weight at 28 days after injury. Retrograde degeneration in spinal cord was associated with decreased density of dendritic branches and presynaptic boutons and increased density of astrocytes and macrophages/microglial cells. Early repair of the injured brachial plexus significantly delayed retrograde degeneration of spinal motoneurons and reduced the degree of macrophage/microglial reaction but had no effect on muscle atrophy. The results demonstrate that early nerve repair of neonatal brachial plexus injury could promote survival of injured motoneurons and attenuate neuroinflammation in spinal cord.

A new numerical method to approximate root water uptake fluxes in a mixed-dimensional 1D-3D setting

2021 ◽  
Author(s):  
Timo Koch ◽  
Hanchuan Wu ◽  
Kent-André Mardal ◽  
Rainer Helmig ◽  
Martin Schneider
Keyword(s):  
Analytical Solutions ◽  
Water Pressure ◽  
Numerical Test ◽  
Three Dimensional ◽  
Root Water Uptake ◽  
Richards Equation ◽  
Soil Pressure ◽  
Approximation Accuracy ◽  
Flux Approximation ◽  
Local Water

<p>1D-3D methods are used to describe root water and nutrient uptake in complex root networks. Root systems are described as networks of line segments embedded in a three-dimensional soil domain. Particularly for dry soils, local water pressure and nutrient concentration gradients can be become very large in the vicinity of roots. Commonly used discretization lengths (for example 1cm) in root-soil interaction models do not allow to capture these gradients accurately. We present a new numerical scheme for approximating root-soil interface fluxes. The scheme is formulated in the continuous PDE setting so that is it formally independent of the spatial discretization scheme (e.g. FVM, FD, FEM). The interface flux approximation is based on a reconstruction of interface quantities using local analytical solutions of the steady-rate Richards equation. The local mass exchange is numerically distributed in the vicinity of the root. The distribution results in a regularization of the soil pressure solution which is easier to approximate numerically. This technique allows for coarser grid resolutions while maintaining approximation accuracy. The new scheme is verified numerically against analytical solutions for simplified cases. We also explore limitations and possible errors in the flux approximation with numerical test cases. Finally, we present the results of a recently published benchmark case using this new method.</p>

Dendrimer applications: a brief review

2021 ◽  
Vol 25 ◽  
Author(s):  
Luis Daniel Pedro-Hernández ◽  
Marcos Martínez-García
Keyword(s):  
Tissue Repair ◽  
Laser Emission ◽  
Catalytic Properties ◽  
Gene Transfection ◽  
Three Dimensional ◽  
Drug Carriers ◽  
The Core ◽  
Controlled Structure ◽  
Dendritic Branches ◽  
Metallic Species

: Dendrimers are highly branched three-dimensional macromolecules with a highly controlled structure, a single molecular weight, numerous controllable dendritic branches and peripheral functionalities, as well as the tendency to adopt an ellipsoid or spheroid shape once a certain size is reached. These features have made them attractive for application in pharmaceutical and medicinal chemistry in gene transfection, as medical imaging agents, and as drug carriers in potential drug delivery agents. The incorporation of metallic species into dendritic molecules has also been reported; the focus has been on organometallic dendrimers with metallic species only at specific positions of the molecules, such as the core, dendritic branches and the periphery, studied for their magnetic, electronic, and photo-optical or catalytic properties. Dendrimers have been investigated for optoelectronic applications (adsorption, emission, laser emission, nonlinear optics) through the encapsulation of active units by dendritic branches, core and peripheral. This review briefly discusses their use in nanomedicine, cancer treatment, treatment of other diseases, tissue repair, catalysis and applications in OLEDs and solar cells.

scholarly journals Evolutionary Dendritic Neural Model for Classification Problems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaoxiao Qian ◽  
Cheng Tang ◽  
Yuki Todo ◽  
Qiuzhen Lin ◽  
Junkai Ji
Keyword(s):  
Neuron Model ◽  
Characteristic Curve ◽  
Cuckoo Search ◽  
Neural Model ◽  
Receiver Operator Characteristic Curve ◽  
Experimental Results ◽  
Logical Circuit ◽  
Classification Problems ◽  
Neural Structure ◽  
Dendritic Branches

In this paper, an evolutionary dendritic neuron model (EDNM) is proposed to solve classification problems. It utilizes synapses and dendritic branches to implement the nonlinear computation. Distinct from the classical dendritic neuron model (CDNM) trained by the backpropagation (BP) algorithm, the proposed EDNM is trained by a metaheuristic cuckoo search (CS) algorithm instead, which has been regarded as a global searching algorithm. CS algorithm enables EDNM to avoid several disadvantages, such as slow convergence, trapping into local minimum, and being sensitive to initial values. To evaluate the performance of EDNM, we compare it with a multilayer perceptron (MLP) and CDNM on two benchmark classification problems. The experimental results demonstrate that EDNM is superior to MLP and CDNM in terms of accuracy rate, receiver operator characteristic curve (ROC), and convergence speed. In addition, the neural structure of EDNM can be replaced by a logical circuit completely, which can be implemented in hardware easily. The corresponding experimental results also verify the effectiveness of the logical circuit classifier.

Biquadratic Digital Phase-Compensator Design with Stability-Margin Controllability

2019 ◽  
Vol 28 (04) ◽  
pp. 1950068 ◽  
Author(s):  
Tian-Bo Deng
Keyword(s):  
Stability Margin ◽  
Transformation Method ◽  
Second Order ◽  
Approximation Accuracy ◽  
Parameter Transformation ◽  
Practical Applications ◽  
Compensator Design ◽  
Novel Method ◽  
Design Idea ◽  
The Stability

This paper proposes a novel method for the design of a recursive second-order (biquadratic) all-pass phase compensator with controllable stability margin. The design idea stems from the generalized stability triangle (GST) derived by the author for the second-order biquadratic digital filter. Based on the GST, a parameter-transformation method is proposed on the transformations of the denominator coefficients of the transfer function of the biquadratic phase compensator. The transformations convert the original denominator coefficients to other new parameters, and any values of those new parameters can guarantee that the GST condition is always satisfied. Optimizing the new parameters yields a biquadratic phase compensator that definitely meets a prespecified stability margin. That is, a biquadratic all-pass phase compensator can be designed to have an arbitrarily specified stability margin. This in turn avoids the occurrence that a recursive phase compensator may become unstable in the practical applications. Thus, the resulting biquadratic phase compensator has robust stability, which is extremely important during the practical filtering operations. A design example is given to show the stability margin guarantee as well as the approximation accuracy.

scholarly journals A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Pengbo Zhang ◽  
Zhixin Yang
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Approximation Accuracy ◽  
Weak Learner ◽  
Fast Learning ◽  
Learning Speed ◽  
Regression Problems ◽  
Learning Machine ◽  
The Stability ◽  
Stability And Accuracy

Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.

Sign in / Sign up

Export Citation Format

Share Document