SEARCH · Searcharxiv

Results for “q-bio”

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42Linked to original sources

arXiv2001

Evolution of networks

We review the recent fast progress in statistical physics of evolving networks. Interest has focused mainly on the structural properties of random complex networks in communications, biology, social sciences and economics. A number of giant artificial networks of such a kind came into existence recently. This opens a wide field for the study of their topology, evolution, and complex processes occurring in them. Such networks possess a rich set of scaling properties. A number of them are scale-free and show striking resilience against random breakdowns. In spite of large sizes of these networks, the distances between most their vertices are short -- a feature known as the ``small-world'' effect. We discuss how growing networks self-organize into scale-free structures and the role of the mechanism of preferential linking. We consider the topological and structural properties of evolving networks, and percolation in these networks. We present a number of models demonstrating the main features of evolving networks and discuss current approaches for their simulation and analytical study. Applications of the general results to particular networks in Nature are discussed. We demonstrate the generic connections of the network growth processes with the general problems of non-equilibrium physics, econophysics, evolutionary biology, etc.

cond-mat.stat-mech↗

arXiv2002

A Simple Chaotic Neuron Model : Stochastic Behavior of Neural Networks

We have shortly reviewed the occurrence of the post-synaptic potentials between neurons, the relation between EEG and neuron dynamics, as well as methods of signal analysis. We supposed a simple stochastic model representing electrical activity of neuronal systems. The model is constructed using the Monte Carlo simulation technique. The results yielded EEG-like signals with their phase portraits in three-dimensional space. The Lyapunov exponent was positive, indicating a chaotic behavior. The correlation dimension of the EEG-like signals was found to be .92, which was smaller than those reported by others. It was concluded that this neuron model may provide valuable clues about the dynamic behavior of neural systems.

cond-mat.stat-mech↗

arXiv1997

A Configurational Bias Monte Carlo Method for Linear and Cyclic Peptides

In this manuscript, we describe a new configurational bias Monte Carlo technique for the simulation of peptides. We focus on the biologically relevant cases of linear and cyclic peptides. Our approach leads to an efficient, Boltzmann-weighted sampling of the torsional degrees of freedom in these biological molecules, a feat not possible with previous Monte Carlo and molecular dynamics methods.

cond-mat.soft↗

arXiv1997

Phase Transitions of Neural Networks

The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network. This is demonstrated for a few examples: Perceptron, associative memory, learning from examples, generalization, multilayer networks, structure recognition, Bayesian estimate, on-line training, noise estimation and time series generation.

cond-mat.dis-nn↗

arXiv1997

Time ordering in the evolution of information processing and modulation systems

The ideas of optimization of learning algorithms in Artificial Neural Networks are reviewed emphasizing generic properties and the online implementations are interpreted from a biological perspective. A simple model of the relevant subsidiary variables needed to improve learning in artificial feedforward networks and the `time ordering' of the appearance of the respective information processing systems is proposed. We discuss the possibility that these results might be relevant in other contexts, not being restricted to the simple models from which they stem. The analysis of a few examples, which range from the lowest cellular scale to the macroscopic level, suggests that similar ideas could be applied to biological systems.

cond-mat.dis-nn↗

arXiv2002

Microspectroscopy and Imaging in the THz Range Using Coherent CW Radiation

A novel THz near-field spectrometer is presented which allows to perform biological and medical studies with high spectral resolution combined with a spatial resolution down to l/100. In the setup an aperture much smaller than the used wavelength is placed in the beam very close to the sample. The sample is probed by the evanescent wave behind the aperture. The distance is measured extremely accurate by a confocal microscope. We use monochromatic sources which provide powerful coherent cw radiation tuneable from 50 GHz up to 1.5 THz. Transmission and reflection experiments can be performed which enable us to study solids and molecules in aqueous solution. Examples for spectroscopic investigations on biological tissues are presented.

physics.bio-ph↗

arXiv1999

Rescaled range and transition matrix analysis of DNA sequences

In this paper we treat some fractal and statistical features of the DNA sequences. First, a fractal record model of DNA sequence is proposed by mapping DNA sequences to integer sequences, followed by R/S analysis of the model and computation of the Hurst exponents. Second, we consider transition between the four kinds of bases within DNA. The transition matrix analysis of DNA sequences shows that some measures of complexity based on transition proportion matrix are of interest. We use some measures of complexity to distinguish exon and intron. Regarding the evolution, we find that for species of higher grade, the transition rate among the four kinds of bases goes further from the equilibrium.

physics.bio-ph↗

arXiv2001

Spectral Representation Theory for Dielectric Behavior of Nonspherical Cell Suspensions

Recent experiments revealed that the dielectric dispersion spectrum of fission yeast cells in a suspension was mainly composed of two sub-dispersions. The low-frequency sub-dispersion depended on the cell length, while the high-frequency one was independent of it. The cell shape effect was simulated by an ellipsoidal cell model but the comparison between theory and experiment was far from being satisfactory. Prompted by the discrepancy, we proposed the use of spectral representation to analyze more realistic cell models. We adopted a shell-spheroidal model to analyze the effects of the cell membrane. It is found that the dielectric property of the cell membrane has only a minor effect on the dispersion magnitude ratio and the characteristic frequency ratio. We further included the effect of rotation of dipole induced by an external electric field, and solved the dipole-rotation spheroidal model in the spectral representation. Good agreement between theory and experiment has been obtained.

cond-mat.soft↗

arXiv2002

Dielectric behavior of oblate spheroidal particles: Application to erythrocytes suspensions

We have investigated the effect of particle shape on the eletrorotation (ER) spectrum of living cells suspensions. In particular, we consider coated oblate spheroidal particles and present a theoretical study of ER based on the spectral representation theory. Analytic expressions for the characteristic frequency as well as the dispersion strength can be obtained, thus simplifying the fitting of experimental data on oblate spheroidal cells that abound in the literature. From the theoretical analysis, we find that the cell shape, coating as well as material parameters can change the ER spectrum. We demonstrate good agreement between our theoretical predictions and experimental data on human erthrocytes suspensions.

cond-mat.soft↗

arXiv2003

Noise in Genotype Selection Model

We study the steady state properties of a genotype selection model in presence of correlated Gaussian white noise. The effect of the noise on the genotype selection model is discussed. It is found that correlated noise can break the balance of gene selection and induce the phase transition which can makes us select one type gene haploid from a gene group.

physics.bio-ph↗

arXiv2003

A Microscopic Mechanism for Muscle's Motion

The SIRM (Stochastic Inclined Rods Model) proposed by H. Matsuura and M. Nakano can explain the muscle's motion perfectly, but the intermolecular potential between myosin head and G-actin is too simple and only repulsive potential is considered. In this paper we study the SIRM with different complex potential and discuss the effect of the spring on the system. The calculation results show that the spring, the effective radius of the G-actin and the intermolecular potential play key roles in the motion. The sliding speed is about $4.7\times10^{-6}m/s$ calculated from the model which well agrees with the experimental data.

physics.bio-ph↗

arXiv1996

Diffusion on a hypercubic lattice with pinning potential: exact results for the error-catastrophe problem in biological evolution

In the theoretical biology framework one fundamental problem is the so-called error catastrophe in Darwinian evolution models. We reexamine Eigen's fundamental equations by mapping them into a polymer depinning transition problem in a ``genotype'' space represented by a unitary hypercubic lattice. The exact solution of the model shows that error catastrophe arises as a direct consequence of the equations involved and confirms some previous qualitative results. The physically relevant consequence is that such equations are not adequate to properly describe evolution of complex life on the Earth.

cond-mat↗

arXiv1998

Entropic Sampling and Natural Selection in Biological Evolution

With a view to connecting random mutation on the molecular level to punctuated equilibrium behavior on the phenotype level, we propose a new model for biological evolution, which incorporates random mutation and natural selection. In this scheme the system evolves continuously into new configurations, yielding non-stationary behavior of the total fitness. Further, both the waiting time distribution of species and the avalanche size distribution display power-law behaviors with exponents close to two, which are consistent with the fossil data. These features are rather robust, indicating the key role of entropy.

cond-mat↗

arXiv1997

An exact learning algorithm for autoassociative neural networks with binary couplings

Exact solutions for the learning problem of autoassociative networks with binary couplings are determined by a new method. The use of a branch-and-bound algorithm leads to a substantial saving of computational time compared with complete enumeration. As a result, fully connected networks with up to 40 neurons could be investigated. The network capacity is found to be close to 0.83.

cond-mat.dis-nn↗

arXiv1996

Error threshold in the evolution of diploid organisms

The effects of error propagation in the reproduction of diploid organisms are studied within the populational genetics framework of the quasispecies model. The dependence of the error threshold on the dominance parameter is fully investigated. In particular, it is shown that dominance can protect the wild-type alleles from the error catastrophe. The analysis is restricted to a diploid analogue of the single-peaked landscape.

cond-mat.dis-nn↗

arXiv1998

Statics and dynamics of an Ashkin-Teller neural network with low loading

An Ashkin-Teller neural network, allowing for two types of neurons is considered in the case of low loading as a function of the strength of the respective couplings between these neurons. The storage and retrieval of embedded patterns built from the two types of neurons, with different degrees of (in)dependence is studied. In particular, thermodynamic properties including the existence and stability of Mattis states are discussed. Furthermore, the dynamic behaviour is examined by deriving flow equations for the macroscopic overlap. It is found that for linked patterns the model shows better retrieval properties than a corresponding Hopfield model.

cond-mat.dis-nn↗

arXiv1998

Phase Diagram and Storage Capacity of Sequence Processing Neural Networks

We solve the dynamics of Hopfield-type neural networks which store sequences of patterns, close to saturation. The asymmetry of the interaction matrix in such models leads to violation of detailed balance, ruling out an equilibrium statistical mechanical analysis. Using generating functional methods we derive exact closed equations for dynamical order parameters, viz. the sequence overlap and correlation- and response functions, in the thermodynamic limit. We calculate the time translation invariant solutions of these equations, describing stationary limit-cycles, which leads to a phase diagram. The effective retarded self-interaction usually appearing in symmetric models is here found to vanish, which causes a significantly enlarged storage capacity of $α_c\sim 0.269$, compared to $α_\c\sim 0.139$ for Hopfield networks storing static patterns. Our results are tested against extensive computer simulations and excellent agreement is found.

cond-mat.dis-nn↗

arXiv1998

Finite-size scaling of the error threshold transition in finite population

The error threshold transition in a stochastic (i.e. finite population) version of the quasispecies model of molecular evolution is studied using finite-size scaling. For the single-sharp-peak replication landscape, the deterministic model exhibits a first-order transition at $Q=Q_c=1/a$, where $% Q$ is the probability of exact replication of a molecule of length $L \to \infty$, and $a$ is the selective advantage of the master string. For sufficiently large population size, $N$, we show that in the critical region the characteristic time for the vanishing of the master strings from the population is described very well by the scaling assumption $τ= N^{1/2} f_a \left [ \left (Q - Q_c) N^{1/2} \right ] $, where $f_a$ is an $a$-dependent scaling function.

cond-mat.stat-mech↗