ORCID Profile
0000-0002-9910-8972
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Artificial Intelligence and Image Processing | Epidemiology | Artificial Life | Simulation and Modelling | Complex Physical Systems
Expanding Knowledge in the Information and Computing Sciences | Social Structure and Health | Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Biological Sciences |
Publisher: MIT Press - Journals
Date: 10-2011
DOI: 10.1162/ARTL_A_00040
Abstract: Small-world networks have been one of the most influential concepts in complex systems science, partly due to their prevalence in naturally occurring networks. It is often suggested that this prevalence is due to an inherent capability to store and transfer information efficiently. We perform an ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies. To generate dynamic behavior for this experiment, we imbue the nodes in these networks with random Boolean functions. We find that the ordered phase of the dynamics (low activity in dynamics) and topologies with low randomness are dominated by information storage, while the chaotic phase (high activity in dynamics) and topologies with high randomness are dominated by information transfer. Information storage and information transfer are somewhat balanced (crossed over) near the small-world regime, providing quantitative evidence that small-world networks do indeed have a propensity to combine comparably large information storage and transfer capacity.
Publisher: MIT Press - Journals
Date: 10-2011
DOI: 10.1162/ARTL_A_00041
Abstract: We study the order-chaos phase transition in random Boolean networks (RBNs), which have been used as models of gene regulatory networks. In particular we seek to characterize the phase diagram in information-theoretic terms, focusing on the effect of the control parameters (activity level and connectivity). Fisher information, which measures how much system dynamics can reveal about the control parameters, offers a natural interpretation of the phase diagram in RBNs. We report that this measure is maximized near the order-chaos phase transitions in RBNs, since this is the region where the system is most sensitive to its parameters. Furthermore, we use this study of RBNs to clarify the relationship between Shannon and Fisher information measures.
Publisher: Hindawi Limited
Date: 19-11-2018
DOI: 10.1155/2018/7306871
Abstract: Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at university level have erse disciplinary backgrounds. This brings challenges (e.g., wide range of computer programming skills) but also opportunities (e.g., facilitating interdisciplinary interactions and projects) for the classroom. However, little has been published regarding how these challenges and opportunities are handled in teaching and learning complex systems as an explicit subject in higher education and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences and analyze them with respect to the educational literature. Our discussions explored approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how ersity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies handling them, for ex le, a significant focus on problem-based learning and the use of major student-led creative projects for both achieving and assessing learning outcomes.
Publisher: American Physical Society (APS)
Date: 15-02-2008
Publisher: The Royal Society
Date: 12-2015
Abstract: We introduce a novel measure, Fisher transfer entropy (FTE), which quantifies a gain in sensitivity to a control parameter of a state transition, in the context of another observable source. The new measure captures both transient and contextual qualities of transfer entropy and the sensitivity characteristics of Fisher information. FTE is exemplified for a ferromagnetic two-dimensional lattice Ising model with Glauber dynamics and is shown to erge at the critical point.
Publisher: American Physical Society (APS)
Date: 16-01-2018
Publisher: American Physical Society (APS)
Date: 24-08-2016
Publisher: MDPI AG
Date: 27-04-2017
DOI: 10.3390/E19050194
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: AIP Publishing
Date: 09-2010
DOI: 10.1063/1.3486801
Abstract: Distributed computation can be described in terms of the fundamental operations of information storage, transfer, and modification. To describe the dynamics of information in computation, we need to quantify these operations on a local scale in space and time. In this paper we extend previous work regarding the local quantification of information storage and transfer, to explore how information modification can be quantified at each spatiotemporal point in a system. We introduce the separable information, a measure which locally identifies information modification events where separate inspection of the sources to a computation is misleading about its outcome. We apply this measure to cellular automata, where it is shown to be the first direct quantitative measure to provide evidence for the long-held conjecture that collisions between emergent particles therein are the dominant information modification events.
Publisher: American Physical Society (APS)
Date: 12-02-2021
Publisher: Optica Publishing Group
Date: 15-07-2001
DOI: 10.1364/OL.26.001042
Abstract: Adiabatically tapered holey fibers are shown to be potentially useful for guided-wave spot-size and numerical-aperture conversion. Conditions for adiabaticity and design guidelines are provided in terms of the effective-index model. We also present finite-difference time-domain calculations of downtapered holey fiber, showing that large spot-size conversion factors are obtainable with minimal loss by use of short, optimally shaped tapers.
Publisher: Springer Science and Business Media LLC
Date: 23-06-2014
DOI: 10.1038/SREP05394
Publisher: Springer Science and Business Media LLC
Date: 2021
Publisher: Public Library of Science (PLoS)
Date: 15-10-2019
Publisher: Springer Science and Business Media LLC
Date: 27-01-2010
Publisher: Elsevier BV
Date: 10-2014
Publisher: American Physical Society (APS)
Date: 13-10-2011
Publisher: Cold Spring Harbor Laboratory
Date: 19-03-2019
DOI: 10.1101/581538
Abstract: A key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a ‘critical’ transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain. Higher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition.
Publisher: Public Library of Science (PLoS)
Date: 12-07-2012
Publisher: Springer Science and Business Media LLC
Date: 25-09-2023
Publisher: The Royal Society
Date: 12-2018
DOI: 10.1098/RSOS.181132
Abstract: Despite the frequency with which mixed-species groups are observed in nature, studies of collective behaviour typically focus on single-species groups. Here, we quantify and compare the patterns of interactions between three fish species, threespine sticklebacks ( Gasterosteus aculeatus ), ninespine sticklebacks ( Pungitius pungitius ) and roach ( Rutilus rutilus ) in both single- and mixed-species shoals in the laboratory. Pilot data confirmed that the three species form both single- and mixed-species shoals in the wild. In our laboratory study, we found that single-species groups were more polarized than mixed-species groups, while single-species groups of threespine sticklebacks and roach were more cohesive than mixed shoals of these species. Furthermore, while there was no difference between the inter-in idual distances between threespine and ninespine sticklebacks within mixed-species groups, there was some evidence of segregation by species in mixed groups of threespine sticklebacks and roach. There were differences between treatments in mean pairwise transfer entropy, and in particular we identify species-differences in information use within the mixed-species groups, and, similarly, differences in responses to conspecifics and heterospecifics in mixed-species groups. We speculate that differences in the patterns of interactions between species in mixed-species groups may determine patterns of fission and fusion in such groups.
Publisher: Springer Science and Business Media LLC
Date: 18-07-2011
Publisher: Springer Science and Business Media LLC
Date: 27-08-2010
DOI: 10.1007/S10827-010-0271-2
Abstract: The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.
Publisher: Springer Science and Business Media LLC
Date: 08-03-2018
Publisher: Springer Science and Business Media LLC
Date: 13-07-2009
Publisher: American Association for the Advancement of Science (AAAS)
Date: 05-10-2018
Abstract: The historic urban collapse of Angkor is linked to infrastructural complexity and climatic variability.
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: American Physical Society (APS)
Date: 27-09-2018
Publisher: Springer Science and Business Media LLC
Date: 27-10-2023
Publisher: Springer Science and Business Media LLC
Date: 22-06-2021
DOI: 10.1038/S41598-021-92170-7
Abstract: Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Science and Business Media LLC
Date: 30-11-2011
DOI: 10.1007/S12064-011-0145-9
Abstract: We have recently presented a framework for the information dynamics of distributed computation that locally identifies the component operations of information storage, transfer, and modification. We have observed that while these component operations exist to some extent in all types of computation, complex computation is distinguished in having coherent structure in its local information dynamics profiles. In this article, we conjecture that coherent information structure is a defining feature of complex computation, particularly in biological systems or artificially evolved computation that solves human-understandable tasks. We present a methodology for studying coherent information structure, consisting of state-space diagrams of the local information dynamics and a measure of structure in these diagrams. The methodology identifies both clear and "hidden" coherent structure in complex computation, most notably reconciling conflicting interpretations of the complexity of the Elementary Cellular Automata rule 22.
Publisher: Springer Science and Business Media LLC
Date: 07-12-2011
DOI: 10.1007/S12064-011-0146-8
Abstract: We investigate information processing in randomly connected recurrent neural networks. It has been shown previously that the computational capabilities of these networks are maximized when the recurrent layer is close to the border between a stable and an unstable dynamics regime, the so called edge of chaos. The reasons, however, for this maximized performance are not completely understood. We adopt an information-theoretical framework and are for the first time able to quantify the computational capabilities between elements of these networks directly as they undergo the phase transition to chaos. Specifically, we present evidence that both information transfer and storage in the recurrent layer are maximized close to this phase transition, providing an explanation for why guiding the recurrent layer toward the edge of chaos is computationally useful. As a consequence, our study suggests self-organized ways of improving performance in recurrent neural networks, driven by input data. Moreover, the networks we study share important features with biological systems such as feedback connections and online computation on input streams. A key ex le is the cerebral cortex, which was shown to also operate close to the edge of chaos. Consequently, the behavior of model systems as studied here is likely to shed light on reasons why biological systems are tuned into this specific regime.
Publisher: Elsevier BV
Date: 07-2013
Publisher: Springer Science and Business Media LLC
Date: 18-07-2011
Publisher: Oxford University Press (OUP)
Date: 19-04-0019
Abstract: Animal groups are often composed of in iduals that vary according to behavioral, morphological, and internal state parameters. Understanding the importance of such in idual-level heterogeneity to the establishment and maintenance of coherent group responses is of fundamental interest in collective behavior. We examined the influence of hunger on the in idual and collective behavior of groups of shoaling fish, x-ray tetras (Pristella maxillaris). Fish were assigned to one of two nutritional states, satiated or hungry, and then allocated to 5 treatments that represented different ratios of satiated to hungry in iduals (8 hungry, 8 satiated, 4:4 hungry:satiated, 2:6 hungry:satiated, 6:2 hungry:satiated). Our data show that groups with a greater proportion of hungry fish swam faster and exhibited greater nearest neighbor distances. Within groups, however, there was no difference in the swimming speeds of hungry versus well-fed fish, suggesting that group members conform and adapt their swimming speed according to the overall composition of the group. We also found significant differences in mean group transfer entropy, suggesting stronger patterns of information flow in groups comprising all, or a majority of, hungry in iduals. In contrast, we did not observe differences in polarization, a measure of group alignment, within groups across treatments. Taken together these results demonstrate that the nutritional state of animals within social groups impacts both in idual and group behavior, and that members of heterogenous groups can adapt their behavior to facilitate coherent collective motion.
Publisher: American Physical Society (APS)
Date: 31-03-2017
Publisher: Elsevier BV
Date: 11-2012
Publisher: Elsevier BV
Date: 12-2019
DOI: 10.1016/J.PLREV.2018.12.003
Abstract: In this paper we explore several fundamental relations between formal systems, algorithms, and dynamical systems, focussing on the roles of undecidability, universality, diagonalization, and self-reference in each of these computational frameworks. Some of these interconnections are well-known, while some are clarified in this study as a result of a fine-grained comparison between recursive formal systems, Turing machines, and Cellular Automata (CAs). In particular, we elaborate on the diagonalization argument applied to distributed computation carried out by CAs, illustrating the key elements of Gödel's proof for CAs. The comparative analysis emphasizes three factors which underlie the capacity to generate undecidable dynamics within the examined computational frameworks: (i) the program-data duality (ii) the potential to access an infinite computational medium and (iii) the ability to implement negation. The considered adaptations of Gödel's proof distinguish between computational universality and undecidability, and show how the diagonalization argument exploits, on several levels, the self-referential basis of undecidability.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2001
DOI: 10.1109/68.935806
Publisher: MDPI AG
Date: 02-2013
DOI: 10.3390/E15020524
Publisher: MIT Press - Journals
Date: 02-2017
DOI: 10.1162/ARTL_A_00221
Abstract: We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team's rigidity and lack of tactical flexibility. The resultant interaction and coherence diagrams reveal implicit interactions, across teams, that may be spatially long-range. The analysis was verified with a statistically significant number of experiments (using simulated football games, produced during RoboCup 2D Simulation League matches), identifying the zones of the most intense competition, the extent and types of interactions, and the correlation between the strength of specific interactions and the results of the matches.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Public Library of Science (PLoS)
Date: 04-03-2014
No related organisations have been discovered for Joseph Lizier.
Start Date: 06-2016
End Date: 12-2019
Amount: $510,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2016
End Date: 12-2019
Amount: $375,000.00
Funder: Australian Research Council
View Funded Activity