scholarly journals Interactions of multiple rhythms in a biophysical network of neurons

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexandros Gelastopoulos ◽  
Nancy J. Kopell
Keyword(s):  
Phase Locking ◽  
Mode Locking ◽  
The Other ◽  
Biophysical Model ◽  
Neural Dynamics ◽  
High Dimensional ◽  
Neural Oscillations ◽  
General Phenomenon ◽  
A Cell ◽  
Dimensional Dynamical System

AbstractNeural oscillations, including rhythms in the beta1 band (12–20 Hz), are important in various cognitive functions. Often neural networks receive rhythmic input at frequencies different from their natural frequency, but very little is known about how such input affects the network’s behavior. We use a simplified, yet biophysical, model of a beta1 rhythm that occurs in the parietal cortex, in order to study its response to oscillatory inputs. We demonstrate that a cell has the ability to respond at the same time to two periodic stimuli of unrelated frequencies, firing in phase with one, but with a mean firing rate equal to that of the other. We show that this is a very general phenomenon, independent of the model used. We next show numerically that the behavior of a different cell, which is modeled as a high-dimensional dynamical system, can be described in a surprisingly simple way, owing to a reset that occurs in the state space when the cell fires. The interaction of the two cells leads to novel combinations of properties for neural dynamics, such as mode-locking to an input without phase-locking to it.

scholarly journals MICRURGICAL STUDIES IN CELL PHYSIOLOGY

1926 ◽  
Vol 10 (1) ◽  
pp. 9-21 ◽  
Author(s):  
Paul Reznikoff
Keyword(s):  
Heavy Metals ◽  
Metal Cation ◽  
Surface Membrane ◽  
The Other ◽  
Contractile Vacuole ◽  
Toxic Action ◽  
Cell Physiology ◽  
Relative Toxicity ◽  
A Cell

I. Plasmalemma. 1. The order of toxicity of the salts used in these experiments on the surface membrane of a cell, taking as a criterion viability of amebæ immersed in solutions for 1 day, is HgCl2, FeCl3> AlCl3> CuCl2> PbCl2> FeCl2. Using viability for 5 days as a criterion, the order of toxicity is PbCl2> CuCl2> HgCl2> AlCl3> FeCl3> FeCl2. 2. The rate of toxicity is in the order FeCl3> HgCl2> AlCl3> FeCl2> CuCl2> PbCl2. 3. The ability of amebæ to recover from a marked tear of the plasmalemma in the solutions of the salts occurred in the following order: AlCl3> PbCl2> FeCl2> CuCl2> FeCl3> HgCl2. II. Internal Protoplasm. 4. The relative toxicity of the salts on the internal protoplasm, judged by the recovery of the amebæ from large injections and the range over which these salts can cause coagulation of the internal protoplasm, is in the following order: PbCl2> CuCl2> FeCl3> HgCl2> FeCl2> AlCl3. 5. AlCl3 in concentrations between M/32 and M/250 causes a marked temporary enlargement of the contractile vacuole. FeCl2, FeCl3, and CuCl3 produce a slight enlargement of the vacuole. 6. PbCl2, in concentrations used in these experiments, appears to form a different type of combination with the internal protoplasm than do the other salts. III. Permeability. 7. Using the similarity in appearance of the internal protoplasm after injection and after immersion to indicate that the surface is permeable to a substance in which the ameba is immersed, it is concluded that AlCl3 can easily penetrate the intact plasmalemma. CuCl2 also seems to have some penetrating power. None of the other salts studied give visible internal evidence of penetrability into the ameba. IV. Toxicity. 8. The toxic action of the chlorides of the heavy metals used in these experiments, and of aluminum, is exerted principally upon the surface of the cell and is due not only to the action of the metal cation but also to acid which is produced by hydrolysis.

scholarly journals Numerical Simulation of a Textured and Untextured Photovoltaic Solar Cell: Comparative study

2021 ◽  
Vol 03 (01) ◽  
pp. 104-114
Author(s):  
Karim Salim ◽  
◽  
M.N Amroun ◽  
K Sahraoui ◽  
W Azzoui ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Solar Cells ◽  
Solar Cell ◽  
Comparative Study ◽  
Silicon Solar Cell ◽  
The Other ◽  
Surface Parameters ◽  
Cell Efficiency ◽  
Photovoltaic Solar Cell ◽  
A Cell

Increasing the efficiency of solar cells relies on the surface of the solar cell. In this work, we simulated a textured silicon solar cell. This simulation allowed us to predict the values of the surface parameters such as the angle and depth between the pyramids for an optimal photovoltaic conversion where we found the Icc: 1.783 (A) and Vco: 0.551 (V) with a cell efficiency of about 13.56%. On the other hand, we performed another simulation of a non-textured solar cell to compare our values and found Icc: 1.623 (A) and Vco: 0.556 (V) with an efficiency of about 12.76%.

scholarly journals Generalization of the Winfree model to the high-dimensional sphere and its emergent dynamics

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Hansol Park
Keyword(s):  
Influence Function ◽  
Equilibrium Solution ◽  
Phase Locking ◽  
Small Diameter ◽  
High Dimensional ◽  
Dimensional Sphere ◽  
Sphere Model ◽  
Dirac Delta ◽  
Delta Distribution ◽  
Winfree Model

<p style='text-indent:20px;'>We present a high-dimensional Winfree model in this paper. The Winfree model is a mathematical model for synchronization on the unit circle. We generalize this model compare to the high-dimensional sphere and we call it the Winfree sphere model. We restricted the support of the influence function in the neighborhood of the attraction point to a small diameter to mimic the influence function as the Dirac delta distribution. We can obtain several new conditions of the complete phase-locking states for the identical Winfree sphere model from restricting the support of the influence function. We also prove the complete oscillator death(COD) state from the exponential <inline-formula><tex-math id="M1">\begin{document}$ \ell^1 $\end{document}</tex-math></inline-formula>-stability and the existence of the equilibrium solution.</p>

scholarly journals Clustered Echo State Networks for Signal Observation and Frequency Filtering

2020 ◽  
Author(s):  
Laércio Oliveira Junior ◽  
Florian Stelzer ◽  
Liang Zhao
Keyword(s):  
Dynamical System ◽  
Neural Networks ◽  
Input Signal ◽  
Recurrent Neural Networks ◽  
High Dimensional ◽  
Frequency Filtering ◽  
Echo State Networks ◽  
Chaotic Signals ◽  
Network Topologies ◽  
Dimensional Dynamical System

Echo State Networks (ESNs) are recurrent neural networks that map an input signal to a high-dimensional dynamical system, called reservoir, and possess adaptive output weights. The output weights are trained such that the ESN’s output signal fits the desired target signal. Classical reservoirs are sparse and randomly connected networks. In this article, we investigate the effect of different network topologies on the performance of ESNs. Specifically, we use two types of networks to construct clustered reservoirs of ESN: the clustered Erdös–Rényi and the clustered Barabási-Albert network model. Moreover, we compare the performance of these clustered ESNs (CESNs) and classical ESNs with the random reservoir by employing them to two different tasks: frequency filtering and the reconstruction of chaotic signals. By using a clustered topology, one can achieve a significant increase in the ESN’s performance.

Tandem Conveyor Queues

1989 ◽  
Vol 3 (4) ◽  
pp. 517-536
Author(s):  
F. Baccelli ◽  
E.G. Coffman ◽  
E.N. Gilbert
Keyword(s):  
Boundary Value Problem ◽  
Joint Distribution ◽  
Queueing System ◽  
The Other ◽  
Riemann Boundary Value Problem ◽  
Riemann Boundary ◽  
Special Cases ◽  
A Cell ◽  
Service Times ◽  
Input Position

This paper analyzes a queueing system in which a constant-speed conveyor brings new items for service and carries away served items. The conveyor is a sequence of cells each able to hold at most one item. At each integer time, a new cell appears at the queue's input position. This cell holds an item requiring service with probability a, holds a passerby requiring no service with probability b, and is empty with probability (1– a – b). Service times are integers synchronized with the arrival of cells at the input, and they are geometrically distributed with parameter μ. Items requiring service are placed in an unbounded queue to await service. Served items are put in a second unbounded queue to await replacement on the conveyor in cells at the input position. Two models are considered. In one, a served item can only be placed into a cell that was empty on arrival; in the other, the served item can be placed into a cell that was either empty or contained an item requiring service (in the latter case unloading and loading at the input position can take place in the same time unit). The stationary joint distribution of the numbers of items in the two queues is studied for both models. It is verified that, in general, this distribution does not have a product form. Explicit results are worked out for special cases, e.g., when b = 0, and when all service times are one time unit (μ = 1). It is shown how the analysis of the general problem can be reduced to the solution of a Riemann boundary-value problem.

Analyse de la morphogenèse du pied des Oiseaux à l'aide de mélanges cellulaires interspécifiques

Development ◽  
1973 ◽  
Vol 29 (1) ◽  
pp. 175-196
Author(s):  
Par Marie-Paule Pautou
Keyword(s):  
Cell Suspension ◽  
The Other ◽  
Composite Type ◽  
A Cell ◽  
Species Specific ◽  
Host Embryo

Morphogenesis of the feet of birds, studied in limbs developed from reaggregated heterospecific mesoderm Experiments were undertaken to determine whether species-specific characters of chick and duck mesodermal leg-bud cells are retained after dissociation and reaggregation in homoand heterospecific mixtures. Prospective zeugopod and autopod mesoderm from chick and/or duck leg buds were isolated, dissociated into a cell suspension and pelleted by centrifugation. The reaggregated mesoderm was packed into a leg-bud ectodermal jacket; the recombined leg bud was then grafted on the wing stump of a host embryo. Recombinants whose mesoderm was a homospecific reaggregate developed into typical chick or duck leg parts according to the specific origin of the mesodermal component; the feet of nearly all these legs lacked antero-posterior polarity. Recombinants containing heterospecific reaggregates were also capable of forming reasonably organized leg structures. The foot was not, as a rule, of the specific type expected of the majority component. In a mixture of 75% chick mesoderm cells and 25% duck mesoderm cells, the feet which developed were either of chick type or of composite chick/duck type, where typical chick areas were next to typical or aberrant (steganoid) duck areas. When the ratio was reversed (25% chick, 75% duck), the majority of the feet were again of chick type or of composite chick/duck type, the typical duck phenotype being exceptional. Even in a mixture of 10% chick cells and 90% duck cells, duck-type feet were not obtained. They were all of composite type: half of their interdigital zones were of chick type, the other half were occupied, in most cases, by underdeveloped, indented webbing or by one or several discrete flaps, and, in a few cases, by normal webbing. The vast majority of the feet developed from heterospecific mesoderm were characterized by the profusion of the toes, which were not polarized along the a–p axis.

Establishment of gut fate in the E lineage of C. elegans: the roles of lineage-dependent mechanisms and cell interactions

Development ◽  
1993 ◽  
Vol 118 (4) ◽  
pp. 1267-1277 ◽  
Author(s):  
B. Goldstein
Keyword(s):  
Cell Interactions ◽  
The Other ◽  
Cell Contact ◽  
Cell Stage ◽  
C Elegans ◽  
Daughter Cells ◽  
Intact Embryo ◽  
A Cell ◽  
Random Position ◽  
Recombination Experiments

The gut of C. elegans derives from all the progeny of the E blastomere, a cell of the eight cell stage. Previous work has shown that gut specification requires an induction during the four cell stage (Goldstein, B. (1992) Nature 357, 255–257). Blastomere isolation and recombination experiments were done to determine which parts of the embryo can respond to gut induction. Normally only the posterior side of the EMS blastomere contacts the inducing cell, P2. When P2 was instead placed in a random position on an isolated EMS, gut consistently differentiated from the daughter of EMS contacting P2, indicating that any side of EMS can respond to gut induction. Additionally, moving P2 around to the opposite side of EMS in an otherwise intact embryo caused EMS's two daughter cells to switch lineage timings, and gut to differentiate from the descendents of what normally would be the MS blastomere. The other cells of the four cell stage, ABa, ABp, and P2, did not form gut when placed in contact with the inducer. To determine whether any other inductions are involved in gut specification, timed blastomere isolations were done at the two and eight cell stages. In the absence of cell contact at the two cell stage, segregation of gut fate proceeded normally at both the two and four cell stages. Gut fate also segregated properly in the absence of cell contact at the eight cell stage. A model is presented for the roles of lineage-dependent mechanisms and cell interactions in establishing gut fate in the E lineage.

Predicting the Chemistry Inside a Cell

2016 ◽  
Author(s):  
Ben McFarland
Keyword(s):  
Scientific Discovery ◽  
The Other ◽  
The South ◽  
Google Maps ◽  
The North ◽  
Closed Circle ◽  
Stone Wall ◽  
Small Stone ◽  
A Cell ◽  
Hadrian's Wall

The process of scientific discovery is something like a walk near Freswick Castle. I assume you’ve never been there. (Neither have I, but a friend has.) Freswick Castle stands at the end of Scotland’s northeast end, at the mouth of the Burn of Freswick in the district of Caithness. As of this writing, it is unlisted in Google Maps, and I had to manually scan the coast to find it. Outside the castle is a simple, unlabeled structure that doubles as a biochemical parable. The castle itself is narrow and three stories tall, with orange shingles and gray stone, set on an arc of narrow beach between hills to the north and cliffs to the south. The building is approximately the cruciform shape of a shrunken cathedral, with the rightward wing moved to the top of the structure so it resembles a lowercase f. If you wander the grounds near Freswick Castle, you will discover a stone wall in the wind-blown waves of yellow- green grass, worn but still standing firm like Hadrian’s Wall. From above, it is a period preceding the castle’s f. Let’s approach this as a scientist, with measurement. From the castle side, this structure resembles the circular stump of a roofless tower, eight feet tall and twice that wide. The stones are ancient sand, compacted and weathered, stained different shades of red from iron deposited millions of years ago, but the mortar is new. But inspection is not enough—we should go in. Walk around to the other side, and an opening appears, as shown in Figure 2.1. The structure is not a closed circle, but it is a spiral wall open to the sea, and to you. Inside, a small stone bench invites you to sit. A window slit next to the bench is an eye to the outside. Surrounded by a jigsaw of rocks, you can hear the echo of waves all around and watch the blue-gray sky above. If the spiral’s opening is a mouth, then you are Jonah in the whale. You are both inside and outside at once.

scholarly journals TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiali Sun ◽  
Qingtai Wu ◽  
Dafeng Shen ◽  
Yangjun Wen ◽  
Fengrong Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
The Other ◽  
New Method ◽  
Genome Wide Association ◽  
High Dimensional ◽  
Simulation Experiments ◽  
Machine Learning Methods ◽  
Least Angle Regression ◽  
Genome Wide

AbstractOne of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods are difficult to analyze the corresponding high-dimensional massive data or SNPs. Recently, machine learning methods have become more popular in high-dimensional genetic data analysis for their fast computation speed. However, most of machine learning methods have several drawbacks, such as poor generalization ability, over-fitting, unsatisfactory classification and low detection accuracy. This study proposed a two-stage algorithm based on least angle regression and random forest (TSLRF), which firstly considered the control of population structure and polygenic effects, then selected the SNPs that were potentially related to target traits by using least angle regression (LARS), furtherly analyzed this variable subset using random forest (RF) to detect quantitative trait nucleotides (QTNs) associated with target traits. The new method has more powerful detection in simulation experiments and real data analyses. The results of simulation experiments showed that, compared with the existing approaches, the new method effectively improved the detection ability of QTNs and model fitting degree, and required less calculation time. In addition, the new method significantly distinguished QTNs and other SNPs. Subsequently, the new method was applied to analyze five flowering-related traits in Arabidopsis. The results showed that, the distinction between QTNs and unrelated SNPs was more significant than the other methods. The new method detected 60 genes confirmed to be related to the target trait, which was significantly higher than the other methods, and simultaneously detected multiple gene clusters associated with the target trait.

LOCAL MOVE IDENTITIES FOR THE ALEXANDER POLYNOMIALS OF HIGH-DIMENSIONAL KNOTS AND INERTIA GROUPS

2009 ◽  
Vol 18 (04) ◽  
pp. 531-545 ◽  
Author(s):  
EIJI OGASA
Keyword(s):  
The Other ◽  
High Dimensional ◽  
Inertia Group ◽  
Alexander Polynomials ◽  
Jones Polynomials

As analogues of the well-known skein relations for the Alexander and the Jones polynomials for classical links, we present three relations that hold among invariants of high dimensional knots differing by "local moves". Two are for the Alexander polynomials and the other is for the Arf-invariants, the inertia group and the bP-subgroup.

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