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We evaluate causal dependencies between thirteen indices that represent large-scale climate patterns (El Nino/Southern Oscillation, the North Atlantic Oscillation, the Pacifc Decadal Oscillation, etc.) using a recently proposed approach based on a linear approximation of the transfer entropy. We demonstrate that this methodology identifes causal relations that are well-known, as well as it uncovers some relations which, to the best of our knowledge, have not yet been reported in the literature. We also identify signifcant changes in causal dependencies that have occurred in the last three decades., Open Access funding provided thanks to the CRUECSIC agreement with Springer Nature. This work received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 813844. C.M. also acknowledges funding by the ICREA ACADEMIA program of Generalitat de Catalunya and Ministerio de Ciencia e Innovacion, Spain, project PID2021-123994NB-C21., Peer Reviewed, Postprint (published version)

BACKGROUND AND HYPOTHESIS: Prior research has shown that patients with schizophrenia (SZ) show disruption in brain network connectivity that is thought to underlie their cognitive and psychotic symptoms. However, most studies examining functional network disruption in schizophrenia have focused on the temporally correlated coupling of the strength of network connections. Here, we move beyond correlative metrics to assay causal computations of connectivity changes in directed neural information flow, assayed from a neural source to a target in SZ. STUDY DESIGN: This study describes a whole-brain magnetoencephalography-imaging approach to examine causal computations of connectivity changes in directed neural information flow between brain regions during resting states, quantified by phase-transfer entropy (PTE) metrics, assayed from a neural source to an endpoint, in 21 SZ compared with 21 healthy controls (HC), and associations with cognitive and clinical psychotic symptoms in SZ. STUDY RESULTS: We found that SZ showed significant disruption in information flow in alpha (8-12 Hz) and beta (12-30 Hz) frequencies, compared to HC. Reduced information flow in alpha frequencies from the precuneus to the medio-ventral occipital cortex was associated with more severe clinical psychopathology (ie, positive psychotic symptoms), while reduced information flow between insula and middle temporal gyrus was associated with worsening cognitive symptoms. CONCLUSIONS: The present findings highlight the importance of delineating dysfunction in neural information flow in specific oscillatory frequencies between distinct regions that underlie the cognitive and psychotic symptoms in SZ, and provide potential neural biomarkers that could lead to innovations in future neuromodulation treatment development.

This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed. A distinction is then drawn between model-based and design-based approaches. Some more recent developments are examined next, namely history-friendly simulation and information-theory based approaches. Finally, in a constructive perspective, structural causal modeling (SCM) is presented, based on the concepts of mechanism and sub-mechanisms, and of recursive decomposition of the joint distribution of variables. This modeling strategy endeavors at representing the structure of the underlying data generating process. It operationalizes the concept of causation through the ordering and role-function of the variables in each of the intelligible sub-mechanisms.

Artificial neural networks (ANNs) are one of the most promising tools in the quest to develop general artificial intelligence. Their design was inspired by how neurons in natural brains connect and process, the only other substrate to harbor intelligence. Compared to biological brains that are sparsely connected and that form sparsely distributed representations, ANNs instead process information by connecting all nodes of one layer to all nodes of the next. In addition, modern ANNs are trained with backpropagation, while their natural counterparts have been optimized by natural evolution over eons. We study whether the training method influences how information propagates through the brain by measuring the transfer entropy, that is, the information that is transferred from one group of neurons to another. We find that while the distribution of connection weights in optimized networks is largely unaffected by the training method, neuroevolution leads to networks in which information transfer is significantly more focused on small groups of neurons (compared to those trained by backpropagation) while also being more robust to perturbations of the weights. We conclude that the specific attributes of a training method (local vs. global) can significantly affect how information is processed and relayed through the brain, even when the overall performance is similar.

Artificial neural networks (ANNs) are one of the most promising tools in the quest to develop general artificial intelligence. Their design was inspired by how neurons in natural brains connect and process, the only other substrate to harbor intelligence. Compared to biological brains that are sparsely connected and that form sparsely distributed representations, ANNs instead process information by connecting all nodes of one layer to all nodes of the next. In addition, modern ANNs are trained with backpropagation, while their natural counterparts have been optimized by natural evolution over eons. We study whether the training method influences how information propagates through the brain by measuring the transfer entropy, that is, the information that is transferred from one group of neurons to another. We find that while the distribution of connection weights in optimized networks is largely unaffected by the training method, neuroevolution leads to networks in which information transfer is significantly more focused on small groups of neurons (compared to those trained by backpropagation) while also being more robust to perturbations of the weights. We conclude that the specific attributes of a training method (local vs. global) can significantly affect how information is processed and relayed through the brain, even when the overall performance is similar.

This study draws on machine learning as a means to causal inference for econometric investigation. We utilize the concept of transfer entropy to examine the relationship between the US National Association of Home Builders Index and the S&P CoreLogic Case-Shiller 20 City Composite Home Price Index (SPCS20). The empirical evidence implies that the survey data can help to predict US house prices. This finding extends the results of Granger causality tests performed by Rodriguez Gonzalez et al. in 2018 using a new machine learning approach that methodologically differs from traditional methods in empirical financial research. © 2021 The Authors. European Financial Management published by John Wiley & Sons Ltd.

Artificial neural networks (ANNs) are one of the most promising tools in the quest to develop general artificial intelligence. Their design was inspired by how neurons in natural brains connect and process, the only other substrate to harbor intelligence. Compared to biological brains that are sparsely connected and that form sparsely distributed representations, ANNs instead process information by connecting all nodes of one layer to all nodes of the next. In addition, modern ANNs are trained with backpropagation, while their natural counterparts have been optimized by natural evolution over eons. We study whether the training method influences how information propagates through the brain by measuring the transfer entropy, that is, the information that is transferred from one group of neurons to another. We find that while the distribution of connection weights in optimized networks is largely unaffected by the training method, neuroevolution leads to networks in which information transfer is significantly more focused on small groups of neurons (compared to those trained by backpropagation) while also being more robust to perturbations of the weights. We conclude that the specific attributes of a training method (local vs. global) can significantly affect how information is processed and relayed through the brain, even when the overall performance is similar.

In collective animal motion, coordination is often achieved by feedback between leaders and followers. For stable coordination, a leader's signals and a follower's responses are hypothesized to be attuned to each other. However, their roles are difficult to disentangle in species with highly coordinated movements, hiding potential diversity of behavioural mechanisms for collective behaviour. Here, we show that two Coptotermes termite species achieve a similar level of coordination via distinct sets of complementary leader–follower interactions. Even though C. gestroi females produce less pheromone than C. formosanus, tandem runs of both species were stable. Heterospecific pairs with C. gestroi males were also stable, but not those with C. formosanus males. We attributed this to the males' adaptation to the conspecific females; C. gestroi males have a unique capacity to follow females with small amounts of pheromone, while C. formosanus males reject C. gestroi females as unsuitable but are competitive over females with large amounts of pheromone. An information-theoretic analysis supported this conclusion by detecting information flow from female to male only in stable tandems. Our study highlights cryptic interspecific variation in movement coordination, a source of novelty for the evolution of social interactions., source:https://royalsocietypublishing.org/doi/10.1098/rspb.2021.0998

[ES] El parto prematuro es una situación de alto riesgo que tiene una prevalencia superior al 10% de los partos, afectando a más de 15 millones de familias en el mundo. Sus repercusiones se muestran tanto en la salud materno fetal, siendo la principal causa de muerte en niños menores de 5 años como en el sobrecoste económico que supone a los sistemas sanitarios de los países. En este trabajo se ha llevado a cabo un estudio comparativo de diferentes medidas de entropía obtenidas de registro no invasivos de la actividad mioeléctrica uterina, electrohisterograma (EHG) en mujeres gestantes que acuden a controles rutinarios del embarazo, para discernir entre el parto a término y prematuro. Con dicho fin han sido analizadas dos bases de datos públicas de registros EHG de mujeres que dieron a luz a término y pretérmino, computándose las siguientes medidas de entropía: entropía muestral, entropía muestral multivariable, entropía difusa, entropía difusa multivariable, entropía de dispersión, entropía de dispersión multivariable, entropía de burbuja y entropía de transferencia. Para cada para cada una de estas medidas se ha realizado un barrido de sus parámetros internos seleccionándose la combinación óptima de los mismos en función de su capacidad para separar entre los dos grupos a discriminar (parto prematuro vs término) de acuerdo con las pruebas estadísticas de Wilconxon y de Kolmogórov-Smirnov. Tras obtener la combinación óptima de parámetros para las diferentes métricas de entropía, se ha valorado el desempeño de un clasificador kNN que emplea estas métricas y otros parámetros temporales y espectrales de las señales de EHG, con el que se ha llegado a obtener un F1 score de 92,23% ± 2,09%., [CA] El part prematur és una situació d'alt risc que té una prevalença superior al 10% dels parts, afectant més de 15 milions de famílies en el món. Les seues repercussions es mostren tant en la salut matern fetal, sent la principal causa de mort en xiquets menors de 5 anys com en el sobrecost econòmic que suposa als sistemes sanitaris dels països. En este treball s'ha dut a terme un estudi comparatiu de diferents mesures d'entropia obtingudes de registre no invasius de l'activitat mioeléctrica uterina, electrohisterograma (EHG) en dones gestants que acudixen a controls rutinaris de l'embaràs, per a discernir entre el part a terme i prematur. Amb el dit fi han sigut analitzades dos bases de dades públiques de registres EHG de dones que van donar a llum a terme i preterme, computant-se les següents mesures d'entropia: entropia mostral, entropia mostral multivariable, entropia difusa, entropia difusa multivariable, entropia de dispersió, entropia de dispersió multivariable, entropia de bambolla i entropia de transferència. Per a cada per a cada una d'estes mesures s'ha realitzat un agranat dels seus paràmetres interns seleccionant-se la combinació òptima dels mateixos en funció de la seua capacitat per a separar entre els dos grups a discriminar (part prematur vs terme) d'acord amb les proves estadístiques de Wilconxon i de Kolmogórov-Smirnov. Després d'obtindre la combinació òptima de paràmetres per a les diferents mètriques d'entropia, s'ha valorat l'exercici d'un classificador kNN que empra estes mètriques i altres paràmetres temporals i espectrals dels senyals d'EHG, amb el que s'ha arribat a obtindre un F1 score de 92,23% ± 2,09%., [EN] The preterm labor is a high-risk situation which has a prevalence up to 10% of all labors, affecting to more than 15 million families worldwide. The consequences are shown both in affected maternal-fetal health, being the main mortality cause in children under 5 years old, and in the economic costs which suppose to the healthcare systems of the countries. In this paper is performed a comparative research among different entropy metrics obtained from non-invasive registers of myoelectric uterine activity, electrohysterography (EHG) in pregnant women who goes to ordinary labor controls, aiming to preterm labor prediction. With this target has been analysed two public EHG register data bases of women who delivered term and preterm, computing the following non-linear metrics: sample entropy, multivariate sample entropy, fuzzy entropy, multivariate fuzzy entropy, dispersion entropy, multivariate dispersion entropy, bubble entropy and transfer entropy. With each of these metrics has been perform a sweep of their internal parameters, selecting the optimal combination regarding their capacity of separate among term and preterm groups, according to the Wilconxon Rank-Sum Test and Kolmogorov-Smirnov distance. When the optimal parameter combination has been gotten, for the different entropy metrics, the performance of an kNN classifier has been assessed using these and other temporal and spectral metrics of EHG signals, getting a F1 score of 92.23% ± 2.09%.

[ES] El parto prematuro es una situación de alto riesgo que tiene una prevalencia superior al 10% de los partos, afectando a más de 15 millones de familias en el mundo. Sus repercusiones se muestran tanto en la salud materno fetal, siendo la principal causa de muerte en niños menores de 5 años como en el sobrecoste económico que supone a los sistemas sanitarios de los países. En este trabajo se ha llevado a cabo un estudio comparativo de diferentes medidas de entropía obtenidas de registro no invasivos de la actividad mioeléctrica uterina, electrohisterograma (EHG) en mujeres gestantes que acuden a controles rutinarios del embarazo, para discernir entre el parto a término y prematuro. Con dicho fin han sido analizadas dos bases de datos públicas de registros EHG de mujeres que dieron a luz a término y pretérmino, computándose las siguientes medidas de entropía: entropía muestral, entropía muestral multivariable, entropía difusa, entropía difusa multivariable, entropía de dispersión, entropía de dispersión multivariable, entropía de burbuja y entropía de transferencia. Para cada para cada una de estas medidas se ha realizado un barrido de sus parámetros internos seleccionándose la combinación óptima de los mismos en función de su capacidad para separar entre los dos grupos a discriminar (parto prematuro vs término) de acuerdo con las pruebas estadísticas de Wilconxon y de Kolmogórov-Smirnov. Tras obtener la combinación óptima de parámetros para las diferentes métricas de entropía, se ha valorado el desempeño de un clasificador kNN que emplea estas métricas y otros parámetros temporales y espectrales de las señales de EHG, con el que se ha llegado a obtener un F1 score de 92,23% ± 2,09%., [CA] El part prematur és una situació d'alt risc que té una prevalença superior al 10% dels parts, afectant més de 15 milions de famílies en el món. Les seues repercussions es mostren tant en la salut matern fetal, sent la principal causa de mort en xiquets menors de 5 anys com en el sobrecost econòmic que suposa als sistemes sanitaris dels països. En este treball s'ha dut a terme un estudi comparatiu de diferents mesures d'entropia obtingudes de registre no invasius de l'activitat mioeléctrica uterina, electrohisterograma (EHG) en dones gestants que acudixen a controls rutinaris de l'embaràs, per a discernir entre el part a terme i prematur. Amb el dit fi han sigut analitzades dos bases de dades públiques de registres EHG de dones que van donar a llum a terme i preterme, computant-se les següents mesures d'entropia: entropia mostral, entropia mostral multivariable, entropia difusa, entropia difusa multivariable, entropia de dispersió, entropia de dispersió multivariable, entropia de bambolla i entropia de transferència. Per a cada per a cada una d'estes mesures s'ha realitzat un agranat dels seus paràmetres interns seleccionant-se la combinació òptima dels mateixos en funció de la seua capacitat per a separar entre els dos grups a discriminar (part prematur vs terme) d'acord amb les proves estadístiques de Wilconxon i de Kolmogórov-Smirnov. Després d'obtindre la combinació òptima de paràmetres per a les diferents mètriques d'entropia, s'ha valorat l'exercici d'un classificador kNN que empra estes mètriques i altres paràmetres temporals i espectrals dels senyals d'EHG, amb el que s'ha arribat a obtindre un F1 score de 92,23% ± 2,09%., [EN] The preterm labor is a high-risk situation which has a prevalence up to 10% of all labors, affecting to more than 15 million families worldwide. The consequences are shown both in affected maternal-fetal health, being the main mortality cause in children under 5 years old, and in the economic costs which suppose to the healthcare systems of the countries. In this paper is performed a comparative research among different entropy metrics obtained from non-invasive registers of myoelectric uterine activity, electrohysterography (EHG) in pregnant women who goes to ordinary labor controls, aiming to preterm labor prediction. With this target has been analysed two public EHG register data bases of women who delivered term and preterm, computing the following non-linear metrics: sample entropy, multivariate sample entropy, fuzzy entropy, multivariate fuzzy entropy, dispersion entropy, multivariate dispersion entropy, bubble entropy and transfer entropy. With each of these metrics has been perform a sweep of their internal parameters, selecting the optimal combination regarding their capacity of separate among term and preterm groups, according to the Wilconxon Rank-Sum Test and Kolmogorov-Smirnov distance. When the optimal parameter combination has been gotten, for the different entropy metrics, the performance of an kNN classifier has been assessed using these and other temporal and spectral metrics of EHG signals, getting a F1 score of 92.23% ± 2.09%.

[ES] El parto prematuro es una situación de alto riesgo que tiene una prevalencia superior al 10% de los partos, afectando a más de 15 millones de familias en el mundo. Sus repercusiones se muestran tanto en la salud materno fetal, siendo la principal causa de muerte en niños menores de 5 años como en el sobrecoste económico que supone a los sistemas sanitarios de los países. En este trabajo se ha llevado a cabo un estudio comparativo de diferentes medidas de entropía obtenidas de registro no invasivos de la actividad mioeléctrica uterina, electrohisterograma (EHG) en mujeres gestantes que acuden a controles rutinarios del embarazo, para discernir entre el parto a término y prematuro. Con dicho fin han sido analizadas dos bases de datos públicas de registros EHG de mujeres que dieron a luz a término y pretérmino, computándose las siguientes medidas de entropía: entropía muestral, entropía muestral multivariable, entropía difusa, entropía difusa multivariable, entropía de dispersión, entropía de dispersión multivariable, entropía de burbuja y entropía de transferencia. Para cada para cada una de estas medidas se ha realizado un barrido de sus parámetros internos seleccionándose la combinación óptima de los mismos en función de su capacidad para separar entre los dos grupos a discriminar (parto prematuro vs término) de acuerdo con las pruebas estadísticas de Wilconxon y de Kolmogórov-Smirnov. Tras obtener la combinación óptima de parámetros para las diferentes métricas de entropía, se ha valorado el desempeño de un clasificador kNN que emplea estas métricas y otros parámetros temporales y espectrales de las señales de EHG, con el que se ha llegado a obtener un F1 score de 92,23% ± 2,09%., [CA] El part prematur és una situació d'alt risc que té una prevalença superior al 10% dels parts, afectant més de 15 milions de famílies en el món. Les seues repercussions es mostren tant en la salut matern fetal, sent la principal causa de mort en xiquets menors de 5 anys com en el sobrecost econòmic que suposa als sistemes sanitaris dels països. En este treball s'ha dut a terme un estudi comparatiu de diferents mesures d'entropia obtingudes de registre no invasius de l'activitat mioeléctrica uterina, electrohisterograma (EHG) en dones gestants que acudixen a controls rutinaris de l'embaràs, per a discernir entre el part a terme i prematur. Amb el dit fi han sigut analitzades dos bases de dades públiques de registres EHG de dones que van donar a llum a terme i preterme, computant-se les següents mesures d'entropia: entropia mostral, entropia mostral multivariable, entropia difusa, entropia difusa multivariable, entropia de dispersió, entropia de dispersió multivariable, entropia de bambolla i entropia de transferència. Per a cada per a cada una d'estes mesures s'ha realitzat un agranat dels seus paràmetres interns seleccionant-se la combinació òptima dels mateixos en funció de la seua capacitat per a separar entre els dos grups a discriminar (part prematur vs terme) d'acord amb les proves estadístiques de Wilconxon i de Kolmogórov-Smirnov. Després d'obtindre la combinació òptima de paràmetres per a les diferents mètriques d'entropia, s'ha valorat l'exercici d'un classificador kNN que empra estes mètriques i altres paràmetres temporals i espectrals dels senyals d'EHG, amb el que s'ha arribat a obtindre un F1 score de 92,23% ± 2,09%., [EN] The preterm labor is a high-risk situation which has a prevalence up to 10% of all labors, affecting to more than 15 million families worldwide. The consequences are shown both in affected maternal-fetal health, being the main mortality cause in children under 5 years old, and in the economic costs which suppose to the healthcare systems of the countries. In this paper is performed a comparative research among different entropy metrics obtained from non-invasive registers of myoelectric uterine activity, electrohysterography (EHG) in pregnant women who goes to ordinary labor controls, aiming to preterm labor prediction. With this target has been analysed two public EHG register data bases of women who delivered term and preterm, computing the following non-linear metrics: sample entropy, multivariate sample entropy, fuzzy entropy, multivariate fuzzy entropy, dispersion entropy, multivariate dispersion entropy, bubble entropy and transfer entropy. With each of these metrics has been perform a sweep of their internal parameters, selecting the optimal combination regarding their capacity of separate among term and preterm groups, according to the Wilconxon Rank-Sum Test and Kolmogorov-Smirnov distance. When the optimal parameter combination has been gotten, for the different entropy metrics, the performance of an kNN classifier has been assessed using these and other temporal and spectral metrics of EHG signals, getting a F1 score of 92.23% ± 2.09%.

[ES] El parto prematuro es una situación de alto riesgo que tiene una prevalencia superior al 10% de los partos, afectando a más de 15 millones de familias en el mundo. Sus repercusiones se muestran tanto en la salud materno fetal, siendo la principal causa de muerte en niños menores de 5 años como en el sobrecoste económico que supone a los sistemas sanitarios de los países. En este trabajo se ha llevado a cabo un estudio comparativo de diferentes medidas de entropía obtenidas de registro no invasivos de la actividad mioeléctrica uterina, electrohisterograma (EHG) en mujeres gestantes que acuden a controles rutinarios del embarazo, para discernir entre el parto a término y prematuro. Con dicho fin han sido analizadas dos bases de datos públicas de registros EHG de mujeres que dieron a luz a término y pretérmino, computándose las siguientes medidas de entropía: entropía muestral, entropía muestral multivariable, entropía difusa, entropía difusa multivariable, entropía de dispersión, entropía de dispersión multivariable, entropía de burbuja y entropía de transferencia. Para cada para cada una de estas medidas se ha realizado un barrido de sus parámetros internos seleccionándose la combinación óptima de los mismos en función de su capacidad para separar entre los dos grupos a discriminar (parto prematuro vs término) de acuerdo con las pruebas estadísticas de Wilconxon y de Kolmogórov-Smirnov. Tras obtener la combinación óptima de parámetros para las diferentes métricas de entropía, se ha valorado el desempeño de un clasificador kNN que emplea estas métricas y otros parámetros temporales y espectrales de las señales de EHG, con el que se ha llegado a obtener un F1 score de 92,23% ± 2,09%., [CA] El part prematur és una situació d'alt risc que té una prevalença superior al 10% dels parts, afectant més de 15 milions de famílies en el món. Les seues repercussions es mostren tant en la salut matern fetal, sent la principal causa de mort en xiquets menors de 5 anys com en el sobrecost econòmic que suposa als sistemes sanitaris dels països. En este treball s'ha dut a terme un estudi comparatiu de diferents mesures d'entropia obtingudes de registre no invasius de l'activitat mioeléctrica uterina, electrohisterograma (EHG) en dones gestants que acudixen a controls rutinaris de l'embaràs, per a discernir entre el part a terme i prematur. Amb el dit fi han sigut analitzades dos bases de dades públiques de registres EHG de dones que van donar a llum a terme i preterme, computant-se les següents mesures d'entropia: entropia mostral, entropia mostral multivariable, entropia difusa, entropia difusa multivariable, entropia de dispersió, entropia de dispersió multivariable, entropia de bambolla i entropia de transferència. Per a cada per a cada una d'estes mesures s'ha realitzat un agranat dels seus paràmetres interns seleccionant-se la combinació òptima dels mateixos en funció de la seua capacitat per a separar entre els dos grups a discriminar (part prematur vs terme) d'acord amb les proves estadístiques de Wilconxon i de Kolmogórov-Smirnov. Després d'obtindre la combinació òptima de paràmetres per a les diferents mètriques d'entropia, s'ha valorat l'exercici d'un classificador kNN que empra estes mètriques i altres paràmetres temporals i espectrals dels senyals d'EHG, amb el que s'ha arribat a obtindre un F1 score de 92,23% ± 2,09%., [EN] The preterm labor is a high-risk situation which has a prevalence up to 10% of all labors, affecting to more than 15 million families worldwide. The consequences are shown both in affected maternal-fetal health, being the main mortality cause in children under 5 years old, and in the economic costs which suppose to the healthcare systems of the countries. In this paper is performed a comparative research among different entropy metrics obtained from non-invasive registers of myoelectric uterine activity, electrohysterography (EHG) in pregnant women who goes to ordinary labor controls, aiming to preterm labor prediction. With this target has been analysed two public EHG register data bases of women who delivered term and preterm, computing the following non-linear metrics: sample entropy, multivariate sample entropy, fuzzy entropy, multivariate fuzzy entropy, dispersion entropy, multivariate dispersion entropy, bubble entropy and transfer entropy. With each of these metrics has been perform a sweep of their internal parameters, selecting the optimal combination regarding their capacity of separate among term and preterm groups, according to the Wilconxon Rank-Sum Test and Kolmogorov-Smirnov distance. When the optimal parameter combination has been gotten, for the different entropy metrics, the performance of an kNN classifier has been assessed using these and other temporal and spectral metrics of EHG signals, getting a F1 score of 92.23% ± 2.09%.

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed strategies to prevent interference, which may lead to different behavior when flying with conspecifics than when flying alone. This study seeks to explore the role of this acoustic sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. This study expands on previous work that only computed information theoretic measures on the 3D position of bats without echolocation calls or that looked at the echolocation calls without using information theoretic analyses. Results show that there is evidence of information transfer between bats flying in pairs when time series for the speed of the bats and their turning behavior are used in the analysis. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader–follower interaction.

Organisms have evolved sensory mechanisms to extract pertinent information from their environment, enabling them to assess their situation and act accordingly. For social organisms travelling in groups, like the fish in a school or the birds in a flock, sharing information can further improve their situational awareness and reaction times. Data on the benefits and costs of social coordination, however, have largely allowed our understanding of why collective behaviours have evolved to outpace our mechanistic knowledge of how they arise. Recent studies have begun to correct this imbalance through fine-scale analyses of group movement data. One approach that has received renewed attention is the use of information theoretic (IT) tools like mutual information, transfer entropy and causation entropy, which can help identify causal interactions in the type of complex, dynamical patterns often on display when organisms act collectively. Yet, there is a communications gap between studies focused on the ecological constraints and solutions of collective action with those demonstrating the promise of IT tools in this arena. We attempt to bridge this divide through a series of ecologically motivated examples designed to illustrate the benefits and challenges of using IT tools to extract deeper insights into the interaction patterns governing group-level dynamics. We summarize some of the approaches taken thus far to circumvent existing challenges in this area and we conclude with an optimistic, yet cautionary perspective.

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Most bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed various strategies to prevent interference which may lead to different behavior when flying with conspecifics than when flying alone. This thesis seeks to explore the role of this sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. Results show that there is evidence of information transfer in both the speed of the bats and their turning behavior, and that such evidence is absent when we consider their heading directions. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader-follower interaction.

Modulation of the amplitude of high-frequency cortical field activity locked to changes in the phase of a slower brain rhythm is known as phase-amplitude coupling (PAC). The study of this phenomenon has been gaining traction in neuroscience because of several reports on its appearance in normal and pathological brain processes in humans as well as across different mammalian species. This has led to the suggestion that PAC may be an intrinsic brain process that facilitates brain inter-area communication across different spatiotemporal scales. Several methods have been proposed to measure the PAC process, but few of these enable detailed study of its time course. It appears that no studies have reported details of PAC dynamics including its possible directional delay characteristic. Here, we study and characterize the use of a novel information theoretic measure that may address this limitation: local transfer entropy. We use both simulated and actual intracranial electroencephalographic data. In both cases, we observe initial indications that local transfer entropy can be used to detect the onset and offset of modulation process periods revealed by mutual information estimated phase-amplitude coupling (MIPAC). We review our results in the context of current theories about PAC in brain electrical activity, and discuss technical issues that must be addressed to see local transfer entropy more widely applied to PAC analysis. The current work sets the foundations for further use of local transfer entropy for estimating PAC process dynamics, and extends and complements our previous work on using local mutual information to compute PAC (MIPAC).

[EN] Ablation of pulmonary veins has emerged as a key procedure for normal rhythm restoration in atrial fibrillation patients. However, up to half of ablated Atrial fibrillation (AF) patients suffer recurrences during the first year. In this article, simultaneous intra-atrial recordings registered at pulmonary veins previous to the ablation procedure were analyzed. Spatial cross-correlation and transfer entropy were computed in order to estimate spatial organization. Results showed that, in patients with arrhythmia recurrence, pulmonary vein electrical activity was less correlated than in patients that maintained sinus rhythm. Moreover, correlation function between dipoles showed higher delays in patients with AF recurrence. Results with transfer entropy were consistent with spatial cross-correlation measurements. These results show that arrhythmia drivers located at the pulmonary veins are associated with a higher organization of the electrical activations after the ablation of these sites.

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed strategies to prevent interference, which may lead to different behavior when flying with conspecifics than when flying alone. This study seeks to explore the role of this acoustic sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. This study expands on previous work that only computed information theoretic measures on the 3D position of bats without echolocation calls or that looked at the echolocation calls without using information theoretic analyses. Results show that there is evidence of information transfer between bats flying in pairs when time series for the speed of the bats and their turning behavior are used in the analysis. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader–follower interaction.

[EN] Ablation of pulmonary veins has emerged as a key procedure for normal rhythm restoration in atrial fibrillation patients. However, up to half of ablated Atrial fibrillation (AF) patients suffer recurrences during the first year. In this article, simultaneous intra-atrial recordings registered at pulmonary veins previous to the ablation procedure were analyzed. Spatial cross-correlation and transfer entropy were computed in order to estimate spatial organization. Results showed that, in patients with arrhythmia recurrence, pulmonary vein electrical activity was less correlated than in patients that maintained sinus rhythm. Moreover, correlation function between dipoles showed higher delays in patients with AF recurrence. Results with transfer entropy were consistent with spatial cross-correlation measurements. These results show that arrhythmia drivers located at the pulmonary veins are associated with a higher organization of the electrical activations after the ablation of these sites.

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed strategies to prevent interference, which may lead to different behavior when flying with conspecifics than when flying alone. This study seeks to explore the role of this acoustic sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. This study expands on previous work that only computed information theoretic measures on the 3D position of bats without echolocation calls or that looked at the echolocation calls without using information theoretic analyses. Results show that there is evidence of information transfer between bats flying in pairs when time series for the speed of the bats and their turning behavior are used in the analysis. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader–follower interaction.

Discovering the correlations among variables of air quality data is challenging, because the correlation time series are long-lasting, multi-faceted, and information-sparse. In this article, we propose a novel visual representation, called Time-correlation-partitioning (TCP) tree, that compactly characterizes correlations of multiple air quality variables and their evolutions. A TCP tree is generated by partitioning the information-theoretic correlation time series into pieces with respect to the variable hierarchy and temporal variations, and reorganizing these pieces into a hierarchically nested structure. The visual exploration of a TCP tree provides a sparse data traversal of the correlation variations and a situation-aware analysis of correlations among variables. This can help meteorologists understand the correlations among air quality variables better. We demonstrate the efficiency of our approach in a real-world air quality investigation scenario. © 2019 Association for Computing Machinery.

The entropy production in stochastic dynamical systems is linked to the structure of their causal representation in terms of Bayesian networks. Such a connection was formalized for bipartite (or multipartite) systems with an integral fluctuation theorem in [Phys. Rev. Lett. 111, 180603 (2013)]. Here we introduce the information thermodynamics for time series, that are non-bipartite in general, and we show that the link between irreversibility and information can only result from an incomplete causal representation. In particular, we consider a backward transfer entropy lower bound to the conditional time series irreversibility that is induced by the absence of feedback in signal-response models. We study such a relation in a linear signal-response model providing analytical solutions, and in a nonlinear biological model of receptor-ligand systems where the time series irreversibility measures the signaling efficiency., Deutsche Forschungsgemeinschaft, Peer Reviewed

The piriform cortex is critically involved in our sense of smell and engages with the olfactory network via specific neural oscillations. It is also considered to be common node of the focal epilepsy network however it is unknown if it plays a role in absence epilepsy. The main focus of this thesis was to determine whether there is any pre-seizure involvement of the piriform cortex in absence epilepsy. Using a rat model of absence epilepsy and non-epileptic controls, multi-channel microelectrode arrays were implanted in the piriform cortex and mediodorsal thalamus, the piriform cortex’s direct link to thalamo-cortical networks underlying absence epilepsy, to record multiunit clusters and field potentials. Three distinct signal processing analyses were developed and applied to the study of the pre-seizure onset connectivity changes in the piriform cortex which encompass four distinct but interrelated research aims. The first aim was to determine the transient firing patterns of multiunit clusters within and between the piriform cortex and mediodorsal thalamus around the onset of absence seizures. The phase locking between multiunit cluster spikes and neural oscillations (spike-LFP phase locking) which govern olfactory network communication was shown to change within the piriform cortex prior to seizure onset, suggesting it contributes to seizure initiation. Early changes in spike-LFP phase locking suggested possible pre-seizure onset changes in local and long range functional connectivity of the piriform cortex and mediodorsal thalamus. A critical issue of analysing functional connectivity using microelectrode arrays is overlapping effect of neighbouring electrodes. Hence the second aim of this thesis was determining the optimal method for resolving the effect of neighbouring electrodes to accurately estimate functional connectivity. Band-amplitude fluctuation orthogonalization followed by inter-site phase clustering estimates was shown to be the optimal method. This

Discovering the correlations among variables of air quality data is challenging, because the correlation time series are long-lasting, multi-faceted, and information-sparse. In this article, we propose a novel visual representation, called Time-correlation-partitioning (TCP) tree, that compactly characterizes correlations of multiple air quality variables and their evolutions. A TCP tree is generated by partitioning the information-theoretic correlation time series into pieces with respect to the variable hierarchy and temporal variations, and reorganizing these pieces into a hierarchically nested structure. The visual exploration of a TCP tree provides a sparse data traversal of the correlation variations and a situation-aware analysis of correlations among variables. This can help meteorologists understand the correlations among air quality variables better. We demonstrate the efficiency of our approach in a real-world air quality investigation scenario. © 2019 Association for Computing Machinery.

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats’ use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats’ turning behavior when they fly in the absence of obstacles.

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats’ use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats’ turning behavior when they fly in the absence of obstacles.

The entropy production in stochastic dynamical systems is linked to the structure of their causal representation in terms of Bayesian networks. Such a connection was formalized for bipartite (or multipartite) systems with an integral fluctuation theorem in [Phys. Rev. Lett. 111, 180603 (2013)]. Here we introduce the information thermodynamics for time series, that are non-bipartite in general, and we show that the link between irreversibility and information can only result from an incomplete causal representation. In particular, we consider a backward transfer entropy lower bound to the conditional time series irreversibility that is induced by the absence of feedback in signal-response models. We study such a relation in a linear signal-response model providing analytical solutions, and in a nonlinear biological model of receptor-ligand systems where the time series irreversibility measures the signaling efficiency., Deutsche Forschungsgemeinschaft, Peer Reviewed

Discovering the correlations among variables of air quality data is challenging, because the correlation time series are long-lasting, multi-faceted, and information-sparse. In this article, we propose a novel visual representation, called Time-correlation-partitioning (TCP) tree, that compactly characterizes correlations of multiple air quality variables and their evolutions. A TCP tree is generated by partitioning the information-theoretic correlation time series into pieces with respect to the variable hierarchy and temporal variations, and reorganizing these pieces into a hierarchically nested structure. The visual exploration of a TCP tree provides a sparse data traversal of the correlation variations and a situation-aware analysis of correlations among variables. This can help meteorologists understand the correlations among air quality variables better. We demonstrate the efficiency of our approach in a real-world air quality investigation scenario. © 2019 Association for Computing Machinery.

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.

Network representation has steadily gained in popularity over the past decades. In many disciplines such as finance, genetics, neuroscience or human travel to cite a few, the network may not directly be observable and needs to be inferred from time-series data, leading to the issue of separating direct interactions between two entities forming the network from indirect interactions coming through its remaining part. Drawing on recent contributions proposing strategies to deal with this problem such as the so-called “global silencing” approach of Barzel and Barabasi or “network deconvolution” of Feizi et al. (2013), we propose a novel methodology to infer an effective network structure from multivariate conditional information transfers. Its core principal is to test the information transfer between two nodes through a step-wise approach by conditioning the transfer for each pair on a specific set of relevant nodes as identified by our algorithm from the rest of the network. The methodology is model free and can be applied to high-dimensional networks with both inter-lag and intra-lag relationships. It outperforms state-of-the-art approaches for eliminating the redundancies and more generally retrieving simulated artificial networks in our Monte-Carlo experiments. We apply the method to stock market data at different frequencies (15 min, 1 h, 1 day) to retrieve the network of US largest financial institutions and then document how bank’s centrality measurements relate to bank’s systemic vulnerability.

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.

Publiziert im Rahmen des KTI Projektes Sales Forecasting

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach., For complete list of authors see http://dx.doi.org/10.3390/e20090627

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach., For complete list of authors see http://dx.doi.org/10.3390/e20090627

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach., QC 20200416

The problem of attack detection in cyber-physical systems is considered in this paper. Transferentropy-based causality countermeasures are introduced for both sensor measurements and innovation sequences, which can be evaluated in a data-driven fashion without relying on a model of the underlying dynamic system. The relationships between the countermeasures and the system parameters as well as the noise statistics are investigated, based on which conditions that guarantee the time convergence of the countermeasures are obtained. The effectiveness of the transfer entropy countermeasures in attack detection is evaluated via theoretical analysis, numerical demonstrations, as well as comparative simulations with classical chi(2) detectors. Four types of attacks are considered: denial-of-service, replay, innovation-based deception, and data injection attacks. Abnormal behavior of the transfer entropy can be observed after the occurrence of each of these attacks., QC 20180222

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach., For complete list of authors see http://dx.doi.org/10.3390/e20090627

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach., For complete list of authors see http://dx.doi.org/10.3390/e20090627

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach., For complete list of authors see http://dx.doi.org/10.3390/e20090627

Publiziert im Rahmen des KTI Projektes Sales Forecasting

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.

In metallic machines ICRH heating is playing an increasingly important role. One of its most recent applications on the Joint Europena Torus (JET) is sawtooth control by ICRH modulation, for avoiding triggering dangerous neo-classical tearing modes (NTMs) and counteracting impurity accumulation. Some of the main difficulties of these experiments are the assessment of the synchronization efficiency and the understanding of the main physical mechanisms at play. In this paper, three independent classes of statistical indicators are introduced to address these issues: Recurrence Plots, Convergent Cross Mapping and Transfer Entropy. The application to JET experiments with the ILW shows that the proposed indicators agree quite well among themselves and provide sound estimates of the efficiency of the synchronisation scheme investigated. They also support, with a shot to shot basis analysis and an estimate of the uncertainties, the interpretation that the fast ions play a fundamental role in the stabilization of the sawteeth, in both L and H mode. Proposals for experiments to be carried out in the future to consolidate the interpretation of the results are discussed., QC 20220922

In metallic machines ICRH heating is playing an increasingly important role. One of its most recent applications on the Joint Europena Torus (JET) is sawtooth control by ICRH modulation, for avoiding triggering dangerous neo-classical tearing modes (NTMs) and counteracting impurity accumulation. Some of the main difficulties of these experiments are the assessment of the synchronization efficiency and the understanding of the main physical mechanisms at play. In this paper, three independent classes of statistical indicators are introduced to address these issues: Recurrence Plots, Convergent Cross Mapping and Transfer Entropy. The application to JET experiments with the ILW shows that the proposed indicators agree quite well among themselves and provide sound estimates of the efficiency of the synchronisation scheme investigated. They also support, with a shot to shot basis analysis and an estimate of the uncertainties, the interpretation that the fast ions play a fundamental role in the stabilization of the sawteeth, in both L and H mode. Proposals for experiments to be carried out in the future to consolidate the interpretation of the results are discussed., For complete list of authors see http://dx.doi.org/10.1088/1741-4326/aa87e7