Analysis of symbolic sequences using the Jensen-Shannon divergence

Große, Ivo; Bernaola‐Galván, Pedro; Carpena, Pedro; Román-Roldán, Ramón; Oliver, José Luis Tejera; Stanley, H. Eugene

doi:10.1103/physreve.65.041905

Cited by 257 publications

(257 citation statements)

References 37 publications

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“…In addition to previously used measures such as the maximum spiking rate (24) and total spike count (9,19) within the time course of the response ( Fig. 2 A and B), we also explored the "total information gain" as measured by D JS , which has been used in time series analysis of discrete events (25) and appeared to be suitable for quantifying differences between spike trains (Fig. 2).…”

Section: Resultsmentioning

confidence: 99%

Post-eclosion odor experience modifies olfactory receptor neuron coding in Drosophila

Iyengar

Chakraborty

Goswami

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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Olfactory responses of Drosophila undergo pronounced changes after eclosion. The flies develop attraction to odors to which they are exposed and aversion to other odors. Behavioral adaptation is correlated with changes in the firing pattern of olfactory receptor neurons (ORNs). In this article, we present an information-theoretic analysis of the firing pattern of ORNs. Flies reared in a synthetic odorless medium were transferred after eclosion to three different media: (i) a synthetic medium relatively devoid of odor cues, (ii) synthetic medium infused with a single odorant, and (iii) complex cornmeal medium rich in odors. Recordings were made from an identified sensillum (type II), and the Jensen-Shannon divergence (D JS ) was used to assess quantitatively the differences between ensemble spike responses to different odors. Analysis shows that prolonged exposure to ethyl acetate and several related esters increases sensitivity to these esters but does not improve the ability of the fly to distinguish between them. Flies exposed to cornmeal display varied sensitivity to these odorants and at the same time develop greater capacity to distinguish between odors. Deprivation of odor experience on an odorless synthetic medium leads to a loss of both sensitivity and acuity. Rich olfactory experience thus helps to shape the ORNs response and enhances its discriminative power. The experiments presented here demonstrate an experience-dependent adaptation at the level of the receptor neuron.imaginal conditioning | sensory adaptation | odor imprinting | JensenShannon divergence | chemo receptor tuning O lfaction in the fruit fly, Drosophila melanogaster, is crucial for a variety of behaviors, including associative learning (1, 2), courtship (3), foraging (4), and flight (5, 6). Odorants are detected by approximately 1,300 olfactory receptor neurons (ORNs), which are housed in sensilla on the third antennal segment and individually express one of approximately 50 functional odor receptors in adults (7-9). All ORNs expressing the same olfactory receptor project to one of approximately 50 glomeruli in the antennal lobe, where they synapse with a set of projection neurons (PNs) (10, 11). The activity of ORNs, either excitation or inhibition, provides behaviorally relevant information about odorants such as their identity, concentration, and source. The information transduced by an ORN is processed in the antennal lobe (12, 13) and sent via PNs to the mushroom bodies, which are believed to be centers for olfactory learning and memory (14, 15).Experience-dependent modifiability (i.e., plasticity) of olfactory representation at the level of the central nervous system is well known (2,16,17). Relatively little attention has been paid to long-term changes in sensory neuron activity resulting from odor experiences. It was previously shown that exposure of newly born imago to a particular set of odorants alters their responses to these odors (18,19). The flies develop an attraction to the chemicals to which they are exposed and an av...

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Section: Resultsmentioning

confidence: 99%

Post-eclosion odor experience modifies olfactory receptor neuron coding in Drosophila

Iyengar

Chakraborty

Goswami

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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“…Grosse et al [35] pointed out that the Jensen-Shannon divergence (JSD) is extremely useful when it comes to discriminate between two (or more) sources. Capra and Singh [34] carefully discussed several information theoretic measures like Shannon entropy, von Neumann entropy, relative entropy, and sum-of-pair measures to assess sequence conservation.…”

Section: Methodsmentioning

confidence: 99%

“…For this aim, we introduce and evaluate a new information theory-based method for the prediction of these residues using Jensen-Shannon divergence (JSD). As a divergence measure based on the Shannon entropy, JSD is a symmetrized and smoothed version of the Kullback-Leibler divergence and is often used for different problems in the field of bioinformatics [31][32][33][34][35]. In this study, following the line of Capra et al [34] we first quantify the divergence between the observed amino acid distribution of a site in a protein and the background distribution of non-binding sites by using JSD.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

Dang

Meckbach

Tacke

et al. 2016

Entropy

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Abstract:The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure-or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen-Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC).

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“…2, the JensenShannon distance between the distributions from sequential UniGen2 and IS is 0.049, while the corresponding figure for parallel UniGen2 and IS is 0.052. These small Jensen-Shannon distances make the distribution of UniGen2 (whether sequential or parallel) indistinguishable from that of IS (See Section IV(C) of [13]). …”

Section: Uniformity Comparisonmentioning

confidence: 99%

On Parallel Scalable Uniform SAT Witness Generation

Chakraborty

Fremont

Meel

et al. 2015

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. Constrained-random verification (CRV) is widely used in industry for validating hardware designs. The effectiveness of CRV depends on the uniformity of test stimuli generated from a given set of constraints. Most existing techniques sacrifice either uniformity or scalability when generating stimuli. While recent work based on random hash functions has shown that it is possible to generate almost uniform stimuli from constraints with 100,000+ variables, the performance still falls short of today's industrial requirements. In this paper, we focus on pushing the performance frontier of uniform stimulus generation further. We present a random hashing-based, easily parallelizable algorithm, UniGen2, for sampling solutions of propositional constraints. UniGen2 provides strong and relevant theoretical guarantees in the context of CRV, while also offering significantly improved performance compared to existing almostuniform generators. Experiments on a diverse set of benchmarks show that UniGen2 achieves an average speedup of about 20× over a state-ofthe-art sampling algorithm, even when running on a single core. Moreover, experiments with multiple cores show that UniGen2 achieves a nearlinear speedup in the number of cores, thereby boosting performance even further.

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Analysis of symbolic sequences using the Jensen-Shannon divergence

Cited by 257 publications

References 37 publications

Post-eclosion odor experience modifies olfactory receptor neuron coding in Drosophila

Post-eclosion odor experience modifies olfactory receptor neuron coding in Drosophila

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

On Parallel Scalable Uniform SAT Witness Generation

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