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At least 361 records · Page 20Linked to original sources
arXiv1999

Spike-train responses of a pair of Hodgkin-Huxley neurons coupled by excitatory and inhibitory synapses and axons with time delay

Numerical calculations have been made on the spike-train response of a pair of Hodgkin-Huxley (HH) neurons coupled by synapses and axons with time delay. The recurrent excitatory-excitatory, inhibitory-inhibitory, excitatory-inhibitory, and inhibitory-excitatory couplings are adopted. The coupled, excitable HH neurons are assumed to receive the two kinds of spike-train inputs: the transient input consisting of M impulses for the finite duration (M: integer) and the sequential input with the constant interspike interval (ISI). The HH neurons in all the kinds of couplings are found to play a role of memory storage with on-off switching. When the coupling strength and the time delay are changed, the distribution of the output ISI $T_{\rm o}$ shows bifurcation (multifurcation), metastability and chaotic behavior. The calculation of the time correlation shows that the synchronization between the two HH neurons is well preserved even when the distribution of their $T_{\rm o}$ is chaotic. The correlation dimension of the cycles of $T_{\rm o}$ is shown to depend not only on the model parameters but also on the type of input ISIs.

cond-mat.dis-nn↗
arXiv1999

Metastability and nucleation in the dilute fluid phase of a simple model of globular proteins

The dilute fluid phase of model globular proteins is studied. The model possesses a fluid-fluid transition buried within the fluid-crystal coexistence region, as do some globular proteins. If this fluid-fluid transition is not buried deep inside the fluid-crystal coexistence region the crystalline phase does not nucleate within the dilute fluid. We link this lack of nucleation of the crystal to the interactions in our model and speculate that similar interactions between globular proteins are responsible for the difficulty found in crystallising many globular proteins.

cond-mat.soft↗
arXiv1999

Three-state neural network: from mutual information to the hamiltonian

The mutual information, I, of the three-state neural network can be obtained exactly for the mean-field architecture, as a function of three macroscopic parameters: the overlap, the neural activity and the {\em activity-overlap}, i.e. the overlap restricted to the active neurons. We perform an expansion of I on the overlap and the activity-overlap, around their values for neurons almost independent on the patterns. From this expansion we obtain an expression for a Hamiltonian which optimizes the retrieval properties of this system. This Hamiltonian has the form of a disordered Blume-Emery-Griffiths model. The dynamics corresponding to this Hamiltonian is found. As a special characteristic of such network, we see that information can survive even if no overlap is present. Hence the basin of attraction of the patterns and the retrieval capacity is much larger than for the Hopfield network. The extreme diluted version is analized, the curves of information are plotted and the phase diagrams are built.

cond-mat.stat-mech↗
arXiv2001

Fitness Uniform Selection to Preserve Genetic Diversity

In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other. We propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure towards sparsely populated fitness regions, not necessarily towards higher fitness, as is the case for all other selection schemes. We show that the new selection scheme can be much more effective than standard selection schemes.

cs.AI↗
arXiv2001

Application of Support Vector Machine to detect an association between a disease or trait and multiple SNP variations

After the completion of human genome sequence was anounced, it is evident that interpretation of DNA sequences is an immediate task to work on. For understanding their signals, improvement of present sequence analysis tools and developing new ones become necessary. Along this current trend, we attack one of the fundamental questions, which set of SNP(single nucleotide polymorphism) variations is related to a specific disease or trait is. For, in the whole DNA sequence, it is known that people have different DNAs only at SNP locations, and moreover, the total SNPs are less than 5 millions, finding an association between SNP variations and certain disease or trait is believed to be one of the essential steps not only for genetic researches but for drug design and discovery. In this paper, we are going to present a method of detecting whether there is an association between multiple SNP variations and a trait or disease. The method exploits the Support Vector Machine which has been attracting lots of attentions recently.

cs.CC↗
arXiv2001

Predicting RNA Secondary Structures with Arbitrary Pseudoknots by Maximizing the Number of Stacking Pairs

The paper investigates the computational problem of predicting RNA secondary structures. The general belief is that allowing pseudoknots makes the problem hard. Existing polynomial-time algorithms are heuristic algorithms with no performance guarantee and can only handle limited types of pseudoknots. In this paper we initiate the study of predicting RNA secondary structures with a maximum number of stacking pairs while allowing arbitrary pseudoknots. We obtain two approximation algorithms with worst-case approximation ratios of 1/2 and 1/3 for planar and general secondary structures,respectively. For an RNA sequence of $n$ bases, the approximation algorithm for planar secondary structures runs in $O(n^3)$ time while that for the general case runs in linear time. Furthermore, we prove that allowing pseudoknots makes it NP-hard to maximize the number of stacking pairs in a planar secondary structure. This result is in contrast with the recent NP-hard results on psuedoknots which are based on optimizing some general and complicated energy functions.

cs.CE↗
arXiv2002

Covariance Plasticity and Regulated Criticality

We propose that a regulation mechanism based on Hebbian covariance plasticity may cause the brain to operate near criticality. We analyze the effect of such a regulation on the dynamics of a network with excitatory and inhibitory neurons and uniform connectivity within and across the two populations. We show that, under broad conditions, the system converges to a critical state lying at the common boundary of three regions in parameter space; these correspond to three modes of behavior: high activity, low activity, oscillation.

cs.NE↗
arXiv2003

Barnacle: An Assembly Algorithm for Clone-based Sequences of Whole Genomes

We propose an assembly algorithm {\sc Barnacle} for sequences generated by the clone-based approach. We illustrate our approach by assembling the human genome. Our novel method abandons the original physical-mapping-first framework. As we show, {\sc Barnacle} more effectively resolves conflicts due to repeated sequences. The latter is the main difficulty of the sequence assembly problem. Inaddition, we are able to detect inconsistencies in the underlying data. We present and compare our results on the December 2001 freeze of the public working draft of the human genome with NCBI's assembly (Build 28). The assembly of December 2001 freeze of the public working draft generated by {\sc Barnacle} and the source code of {\sc Barnacle} are available at (http://www.cs.rutgers.edu/~vchoi).

cs.DS↗
arXiv2003

A Representation of Changes of Images and its Application for Developmental Biolology

In this paper, we consider a series of events observed at spaced time intervals and present a method of representation of the series. To explain an idea, by dealing with a set of gene expression data, which could be obtained from developmental biology, the procedures are sketched with comments in some details. We mean representation by choosing a proper function, which fits well with observed data of a series, and turning its characteristics into numbers, which extract the intrinsic properties of fluctuating data. With help of a machine learning techniques, this method will give a classification of developmental biological data as well as any varying data during a certain period and the classification can be applied for diagnosis of a disease.

cs.CC↗
arXiv2003

A family of metrics on contact structures based on edge ideals

The measurement of the similarity of RNA secondary structures, and in general of contact structures, of a fixed length has several specific applications. For instance, it is used in the analysis of the ensemble of suboptimal secondary structures generated by a given algorithm on a given RNA sequence, and in the comparison of the secondary structures predicted by different algorithms on a given RNA molecule. It is also a useful tool in the quantitative study of sequence-structure maps. A way to measure this similarity is by means of metrics. In this paper we introduce a new class of metrics $d_{m}$, $m\geq 3$, on the set of all contact structures of a fixed length, based on their representation by means of edge ideals in a polynomial ring. These metrics can be expressed in terms of Hilbert functions of monomial ideals, which allows the use of several public domain computer algebra systems to compute them. We study some abstract properties of these metrics, and we obtain explicit descriptions of them for $m=3,4$ on arbitrary contact structures and for $m=5,6$ on RNA secondary structures.

cs.DM↗
arXiv1995

On the Informativeness of the DNA Promoter Sequences Domain Theory

The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory.

cs.AI↗
arXiv1997

Analysis of Three-Dimensional Protein Images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.

cs.AI↗
arXiv1999

SIMMUNE, a tool for simulating and analyzing immune system behavior

We present a new approach to the simulation and analysis of immune system behavior. The simulations that can be done with our software package called SIMMUNE are based on immunological data that describe the behavior of immune system agents (cells, molecules) on a microscopial (i.e. agent-agent interaction) scale by defining cellular stimulus-response mechanisms. Since the behavior of the agents in SIMMUNE can be very flexibly configured, its application is not limited to immune system simulations. We outline the principles of SIMMUNE's multiscale analysis of emergent structure within the simulated immune system that allow the identification of immunological contexts using minimal a priori assumptions about the higher level organization of the immune system.

cs.MA↗
arXiv1999

Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Pre-Cancer

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, non-invasively and quantitatively probes the biochemical and morphological changes that occur in pre-cancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337, 380 and 460 nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from pre-cancerous tissue samples. The use of connectionist methods such as multi layered perceptrons, radial basis function networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated, and near real-time implementation of pre-cancer detection in the hands of non-experts. The results are more reliable, direct and accurate than those achieved by either human experts or multivariate statistical algorithms.

cs.NE↗
arXiv1994

Gauge theory of things alive and universal dynamics

Positing complex adaptive systems made of agents with relations between them that can be composed, it follows that they can be described by gauge theories similar to elementary particle theory and general relativity. By definition, a universal dynamics is able to determine the time development of any such system without need for further specification. The possibilities are limited, but one of them - reproduction fork dynamics - describes DNA replication and is the basis of biological life on earth. It is a universal copy machine and a renormalization group fixed point. A universal equation of motion in continuous time is also presented.

hep-lat↗