by Keyword: Complexity

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Good, M., Trepat, X., (2018). Cell parts to complex processes, from the bottom up Nature 563, (7730), 188-189

Engineering approaches allow biological structures and behaviours to be reconstituted in vitro. A biologist and a physicist discuss the potential and limitations of this bottom-up philosophy in providing insights into complex biological processes.

Keywords: Biophysics, Complexity, Engineering

Arsiwalla, Xerxes D., Verschure, Paul, (2018). Measuring the complexity of consciousness Frontiers in Neuroscience , 12, (424), Article 424

The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.

Keywords: Consciousness in the Clinic, Computational neuroscience, Complexity measures, Clinical Neuroscience, Measures of consciousness

Arsiwalla, X. D., Pacheco, D., Principe, A., Rocamora, R., Verschure, P., (2018). A temporal estimate of integrated information for intracranial functional connectivity Artificial Neural Networks and Machine Learning (Lecture Notes in Computer Science) 27th International Conference on Artificial Neural Networks (ICANN 2018) , Springer, Cham (Rhodes, Greece) 11140, 403-412

A major challenge in computational and systems neuroscience concerns the quantification of information processing at various scales of the brain’s anatomy. In particular, using human intracranial recordings, the question we ask in this paper is: How can we estimate the informational complexity of the brain given the complex temporal nature of its dynamics? To address this we work with a recent formulation of network integrated information that is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. In this work, we extend this formulation for temporal networks and then apply it to human brain data obtained from intracranial recordings in epilepsy patients. Our findings show that compared to random re-wirings of the data, functional connectivity networks, constructed from human brain data, score consistently higher in the above measure of integrated information. This work suggests that temporal integrated information may indeed be a good starting point as a future measure of cognitive complexity.

Keywords: Brain networks, Complexity measures, Computational neuroscience, Functional connectivity

Arsiwalla, X. D., Signorelli, C. M., Puigbo, J. Y., Freire, I. T., Verschure, P., (2018). What is the physics of intelligence? Frontiers in Artificial Intelligence and Applications (ed. Falomir, Z., Gibert, K., Plaza, E.), IOS Press (Amsterdam, The Netherlands) Volume 308: Artificial Intelligence Research and Development, 283-286

In the absence of a first-principles definition, the concept of intelligence is often specified in terms of its phenomenological functions as a capacity or ability to solve problems autonomously. Whenever an agent, biological or artificial, possesses this ability, it is considered intelligent, otherwise not. While this description serves as a useful correlate of intelligence, it is far from a principled explanation that provides a general, yet precise definition along with predictions of mechanisms leading to intelligent behavior. We do not want an explanation to depend on any functionality that itself might be a consequence of intelligence. A possible conceptualization of a function-free approach might be to formulate the concept in terms of dynamical information complexity. This constitute a first step towards a statistical mechanics theory of intelligence. In this paper, we outline the steps towards a physics-based definition of intelligence.

Keywords: Complexity, Information Theory, Physics of Intelligence

Marco, Santiago, (2011). Signal processing for chemical sensing: Statistics or biological inspiration Olfaction and Electronic Nose: Proceedings of the 14th International Symposium on Olfaction and Electronic Nose AIP Conference Proceedings (ed. Perena Gouma, SUNY Stony Brook), AIP (New York City, USA) 1362, (1), 145-146

Current analytical instrumentation and continuous sensing can provide huge amounts of data. Automatic signal processing and information evaluation is needed to overcome drowning in data. Today, statistical techniques are typically used to analyse and extract information from continuous signals. However, it is very interesting to note that biology (insects and vertebrates) has found alternative solutions for chemical sensing and information processing. This is a brief introduction to the developments in the European Project: Bio-ICT NEUROCHEM: Biologically Inspired Computation for Chemical Sensing (grant no. 216916) Fp7 project devoted to biomimetic olfactory systems.

Keywords: Signal processing, Chemioception, Neural nets, Computational complexity

Correa, L. S., Laciar, E., Mut, V., Giraldo, B. F., Torres, A., (2010). Multi-parameter analysis of ECG and Respiratory Flow signals to identify success of patients on weaning trials Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) -----, 6070-6073

Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. It was found that mean values of cardiac inter-beat and breath durations give higher values for SG than for FG; Kurtosis coefficient of the spectrum of the rapid shallow breathing index is higher for FG; also Lempel Ziv complexity mean values associated with the respiratory flow signal are bigger for FG. Patients were then classified with a pattern recognition neural network, obtaining 80% of correct classifications (81.6% for FG and 79.5% for SG).

Keywords: Electrocardiography, Medical signal processing, Neural nets, Pattern recognition, Pneumodynamics, Signal classification, Statistical analysis, ECG, Kurtosis coefficient, Lempel Ziv complexity, Breath durations, Cardiac interbeat durations, Electrocardiography, Multiparameter analysis, Pattern recognition neural network, Power spectral density, Respiratory flow signals, Signal classification, Spontaneous breathing, Statistical analysis, Weaning trials