380 research outputs found
Towards adaptive clustering in self-monitoring multi-agent networks
No full textPiraveenan Mahendra rajah, Mikhail Prokopenko, Peter Wang, Don Pric
Measuring information dynamics in swarms
No full textWe propose a novel, information theoretic characterization of dynamics within swarms, through explicitly measuring the extent of collective communications and tracing collectivememory. These elements of distributed computation provide complementary views into the capacity for swarm coherence and reorganization. The approach deals with both global and local information dynamics ultimately discovering diverse ways in which an individual’s location within the group is related to its information processing role.22 page(s
Supplementary code and data for "The effects of local homogeneity assumptions in metapopulation models of infectious disease"
No full textThis dataset contains both simulation software and input/output data to accompany the paper
"The effects of local homogeneity assumptions in metapopulation models of infectious disease"
by Cameron Zachreson, Sheryl Chang, Nathan Harding, and Mikhail Prokopenko
On the cross-disciplinary nature of guided self-organisation
No full textSelf-organisation is pervasive: neuronal ensembles self-organise into complex spatio-temporal spike patterns which facilitate synaptic plasticity and long-term consolidation of information; large-scale natural or social systems, as diverse as forest fires, landslides, or epidemics, produce spontaneous scale-invariant behaviour; robotic modules self-organise into coordinated motion patterns; individuals within a swarm achieve collective coherence out of isolated actions; and so on. Selforganisation is also valuable: the resultant increase in an internal organisation brings benefits to the (collective) organism, be it a learning brain, a co-evolving ecosystem, an adapting modular robot, or a re-configuring swarm. These benefits are typically realised in increased resilience to external disturbances, adaptivity to novel tasks, and scalability with respect to new challenges. However, self-organisation is difficult to engineer on demand: the intricate fabric of interactions within a self-organising system cannot follow a simple-minded blueprint and resists crude interventions.13 page(s
A Framework for the local information dynamics of distributed computation in complex systems
No full textThe nature of distributed computation has long been a topic of interest in complex systems science, physics, artificial life and bioinformatics. In particular, emergent complex behavior has often been described from the perspective of computation within the system (Mitchell 1998b,a) and has been postulated to be associated with the capability to support universal computation (Langton 1990; Wolfram 1984c; Casti 1991).44 page(s
Mikhail Prokopenko*
No full textAbstract We consider a hierarchical multicellular sensing and communication network, embedded in an ageless aerospace vehicle, that is expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and impact networks connecting remote cells that have detected noncritical impacts. Each level of the hierarchy is shown to have distinct higher-order emergent properties, desirable in self-monitoring and self-repairing vehicles. In addition, cells and communication messages are shown to need memory (hysteresis) in order to retain desirable emergent behavior within and between various hierarchical levels. Spatiotemporal robustness of self-organizing hierarchies is quantitatively measured with graph-theoretic and information-theoretic techniques, such as the Shannon entropy. This allows us to clearly identify phase transitions separatin
Modern aspects of the use of foggy technologies in the educational process of higher educational institutions
No full textProkopenko A. Modern aspects of the use of foggy technologies in the educational process of higher educational institutions / A. Prokopenko // Освітні Обрії / Івано-Франків. обл. ін-т післядиплом. освіти пед. працівників. – 2020. – Вип. 1(50). – С. 98–101. Фахове виданняThe article considers the issues of improving the managerial and educational activities of higher education institutions through the use of cloudy and foggy innovative technologies. The author of the article on the basis of the analysis of scientific, pedagogical and specialized literature proves the relevance of research in this direction. This scientific legacy presents Google services that are appropriate to use in the educational process to increase its effectiveness and efficiency, as well as the feasibility of organizing and implementing an educational and scientific laboratory of innovative technologies
Quantifying Synergistic Mutual Information
No full textSynergy is a fundamental concept in complex systems that has received much attention in computational biology (Narayanan et al. 2005; Balduzzi and Tononi 2008). Several papers (Schneidman et al. 2003a; Bell 2003; Nirenberg et al. 2001;Williams and Beer 2010) have proposed measures for quantifying synergy, but there remains no consensus which measure is most valid
Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules
No full textGenerating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational intelligence.We propose and explore a new objective function, which allows to obtain plasticity rules for the afferent synaptic weights. The adaption rules are Hebbian, self-limiting, and result from the minimization of the Fisher information with respect to the synaptic flux. We perform a series of simulations examining the behavior of the new learning rules in various circumstances.The vector of synaptic weights aligns with the principal direction of input activities, whenever one is present. A linear discrimination is performed when there are two or more principal directions; directions having bimodal firing-rate distributions, being characterized by a negative excess kurtosis, are preferred. We find robust performance and full homeostatic adaption of the synaptic weights results as a by-product of the synaptic flux minimization. This self-limiting behavior allows for stable online learning for arbitrary durations.The neuron acquires new information when the statistics of input activities is changed at a certain point of the simulation, showing however, a distinct resilience to unlearn previously acquired knowledge. Learning is fast when starting with randomly drawn synaptic weights and substantially slower when the synaptic weights are already fully adapted
Generalised SIRS network (multi-city) model of spatial contagion
No full text<p>Spatial contagions, such as pandemics, opinion polarisation, infodemics, and civil unrest, exhibit nontrivial spatiotemporal dynamics driven by complex human behaviours and population mobility. Here we propose a concise generic framework to model different contagion types within a suitably defined behavioural space. The behavioural traits are considered in terms of bounded rationality, and comprise confirmation bias and adaptive dissent, mapped against individualism and collective behaviour, and complemented by generalised susceptibility acquisition. Resultant spatial contagion configurations follow intricate Turing patterns observed in reaction-diffusion systems. Pattern formation is shown to be highly sensitive to changes in underlying behavioural parameters. The identified critical regimes (tipping points) imply that slight changes in susceptibility acquisition and perceived local risks can significantly alter the population flow and resultant contagion patterns. The proposed framework enables development of tools improving prediction and mitigation of contagion-specific dynamics and their associated socio-political and socio-economic impacts, and contributing to stronger social cohesion.</p><p>The paper describing the framework, model and results:</p><p>C. M. Jamerlan, M. Prokopenko. Wisdom and madness of crowds: Susceptibility and tipping points during spatial contagions. <i>Manuscript under review </i>(2023).</p><p>Please cite this paper and references below when using the model.</p>
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