uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Perna, Andrea
Publications (8 of 8) Show all publications
Vogel, D., Gautrais, J., Perna, A., Sumpter, D. J. T., Deneubourg, J.-L. & Dussutour, A. (2017). Transition from isotropic to digitated growth modulates network formation in Physarum polycephalum. Journal of Physics D: Applied Physics, 50(1), Article ID 014002.
Open this publication in new window or tab >>Transition from isotropic to digitated growth modulates network formation in Physarum polycephalum
Show others...
2017 (English)In: Journal of Physics D: Applied Physics, ISSN 0022-3727, E-ISSN 1361-6463, Vol. 50, no 1, article id 014002Article in journal (Refereed) Published
Abstract [en]

Some organisms, including fungi, ants, and slime molds, explore their environment and forage by forming interconnected networks. The plasmodium of the slime mold Physarum polycephalum is a large unicellular amoeboid organism that grows a tubular spatial network through which nutrients, body mass, and chemical signals are transported. Individual plasmodia are capable of sophisticated behaviours such as optimizing their network connectivity and dynamics using only decentralized information processing. In this study, we used a population of plasmodia that interconnect through time to analyse the dynamical interactions between growth of individual plasmodia and global network formation. Our results showed how initial conditions, such as the distance between plasmodia, their size, or the presence and quality of food, affect the emerging network connectivity.

Keywords
transportation networks, slime molds, exploration, foraging, pattern formation
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-311475 (URN)10.1088/1361-6463/50/1/014002 (DOI)000389050000001 ()
Funder
EU, European Research Council, IDCAB 220/104702003
Available from: 2016-12-29 Created: 2016-12-28 Last updated: 2018-01-13Bibliographically approved
Viana, M. P., Fourcassie, V., Perna, A., Costa, L. d. & Jost, C. (2013). Accessibility in networks: A useful measure for understanding social insect nest architecture. Chaos, Solitons & Fractals, 46, 38-45
Open this publication in new window or tab >>Accessibility in networks: A useful measure for understanding social insect nest architecture
Show others...
2013 (English)In: Chaos, Solitons & Fractals, ISSN 0960-0779, E-ISSN 1873-2887, Vol. 46, p. 38-45Article in journal (Refereed) Published
Abstract [en]

Networks and the associated tools from graph theory have now become well-established approaches to study natural as well as human-made systems. While early studies focused on topology and connectivity, the recent literature has acknowledged the importance of the dynamical properties of these networks. Here we focus on such a dynamic measure: accessibility. It characterizes for any given movement dynamics (such as random walks) the average number of nodes that can be reached in exactly h steps (out-accessibility), or the average number of nodes from which a given node can be reached (in-accessibility). This focus on dynamics makes accessibility particularly appropriate to study movement on networks and to detect complementary properties with respect to topology-based measurements such as betweenness centrality. We apply this measure to six nests of Cubitermes termites. Their mushroom-like 3D architectures consist of chambers and connecting tunnels that can be associated to nodes and edges in a communication network. Accessibilities turn out to be particularly low in the bottom part of the nests that link them to their underground tunneling network. We interpret this result in the context of anti-predator (ants) behavior and/or as a side effect of the global nest shape.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-195611 (URN)10.1016/j.chaos.2012.11.003 (DOI)000314010700005 ()
Available from: 2013-02-27 Created: 2013-02-26 Last updated: 2017-12-06Bibliographically approved
Pettit, B., Perna, A., Biro, D. & Sumpter, D. J. T. (2013). Interaction rules underlying group decisions in homing pigeons. Journal of the Royal Society Interface, 10(89), 20130529
Open this publication in new window or tab >>Interaction rules underlying group decisions in homing pigeons
2013 (English)In: Journal of the Royal Society Interface, ISSN 1742-5689, E-ISSN 1742-5662, Vol. 10, no 89, p. 20130529-Article in journal (Refereed) Published
Abstract [en]

Travelling in groups gives animals opportunities to share route information by following cues from each other's movement. The outcome of group navigation will depend on how individuals respond to each other within a flock, school, swarm or herd. Despite the abundance of modelling studies, only recently have researchers developed techniques to determine the interaction rules among real animals. Here, we use high-resolution GPS (global positioning system) tracking to study these interactions in pairs of pigeons flying home from a familiar site. Momentary changes in velocity indicate alignment with the neighbour's direction, as well as attraction or avoidance depending on distance. Responses were stronger when the neighbour was in front. From the flocking behaviour, we develop a model to predict features of group navigation. Specifically, we show that the interactions between pigeons stabilize a side-by-side configuration, promoting bidirectional information transfer and reducing the risk of separation. However, if one bird gets in front it will lead directional choices. Our model further predicts, and observations confirm, that a faster bird (as measured from solo flights) will fly slightly in front and thus dominate the choice of homing route. Our results explain how group decisions emerge from individual differences in homing flight behaviour.

Keywords
collective animal behaviour, leadership, bird flocks, collective decision-making, self-propelled particles
National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-219983 (URN)10.1098/rsif.2013.0529 (DOI)000330302400001 ()
Available from: 2014-03-11 Created: 2014-03-09 Last updated: 2017-12-05Bibliographically approved
Perna, A., Granovskiy, B., Garnier, S., Nicolis, S. C., Labédan, M., Theraulaz, G., . . . Sumpter, D. J. T. (2012). Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile). PloS Computational Biology, 8(7), e1002592
Open this publication in new window or tab >>Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile)
Show others...
2012 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 8, no 7, p. e1002592-Article in journal (Refereed) Published
Abstract [en]

Many ant species produce large dendritic networks of trails around their nest. These networks result from self-organized feedback mechanisms: ants leave small amounts of a chemical -a pheromone- as they move across space. In turn, they are attracted by this same pheromone so that eventually a trail is formed. In our study, we introduce a new image analysis technique to estimate the concentrations of pheromone directly on the trails. In this way, we can characterise the ingredients of the feedback loop that ultimately leads to the formation of trails. We show that the response to pheromone concentrations is linear: an ant will turn to the left with frequency proportional to the difference between the pheromone concentrations on its left and right sides. Such a linear individual response was rejected by previous literature, as it would be incompatible with the results of a large number of experiments: trails can only be reinforced if the ants have a disproportionally higher probability to select the trail with higher pheromone concentration. However, we show that the required non-linearity does not reside in the perceptual response of the ants, but in the noise associated with their movement.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-180968 (URN)10.1371/journal.pcbi.1002592 (DOI)000306842200017 ()
Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2017-12-07Bibliographically approved
Mann, R. P., Perna, A., Strömbom, D., Garnett, R., Herbert-Read, J. E., Sumpter, D. J. T. & Ward, A. J. W. (2012). Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection. PloS Computational Biology, 8(1), e1002308
Open this publication in new window or tab >>Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection
Show others...
2012 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 8, no 1, p. e1002308-Article in journal (Refereed) Published
Abstract [en]

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-171491 (URN)10.1371/journal.pcbi.1002308 (DOI)000300218100005 ()
Note

Retraction in: PLOS COMPUTATIONAL BIOLOGY  Volume: 9 Issue: 3 Article Number: e1002961   

DOI: 10.1371/journal.pcbi.1002961

Available from: 2012-03-20 Created: 2012-03-20 Last updated: 2017-12-07Bibliographically approved
Sumpter, D. J. T., Mann, R. P. & Perna, A. (2012). The modelling cycle for collective animal behaviour. INTERFACE FOCUS, 2(6), 764-773
Open this publication in new window or tab >>The modelling cycle for collective animal behaviour
2012 (English)In: INTERFACE FOCUS, ISSN 2042-8898, Vol. 2, no 6, p. 764-773Article, review/survey (Refereed) Published
Abstract [en]

Collective animal behaviour is the study of how interactions between individuals produce group level patterns, and why these interactions have evolved. This study has proved itself uniquely interdisciplinary, involving physicists, mathematicians, engineers as well as biologists. Almost all experimental work in this area is related directly or indirectly to mathematical models, with regular movement back and forth between models, experimental data and statistical fitting. In this paper, we describe how the modelling cycle works in the study of collective animal behaviour. We classify studies as addressing questions at different levels or linking different levels, i.e. as local, local to global, global to local or global. We also describe three distinct approaches-theory-driven, data-driven and model selection-to these questions. We show, with reference to our own research on species across different taxa, how we move between these different levels of description and how these various approaches can be applied to link levels together.

Keywords
collective animal behaviour, theoretical modelling, collective motion
National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-187220 (URN)10.1098/rsfs.2012.0031 (DOI)000310522300009 ()
Available from: 2012-12-06 Created: 2012-12-04 Last updated: 2012-12-06Bibliographically approved
Herbert-Read, J. E., Perna, A., Mann, R. P., Schaerf, T. M., Sumpter, D. J. T. & Ward, A. J. W. (2011). Inferring the rules of interaction of shoaling fish. Proceedings of the National Academy of Sciences of the United States of America, 108(46), 18726-18731
Open this publication in new window or tab >>Inferring the rules of interaction of shoaling fish
Show others...
2011 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 108, no 46, p. 18726-18731Article in journal (Refereed) Published
Abstract [en]

Collective motion, where large numbers of individuals move synchronously together, is achieved when individuals adopt interaction rules that determine how they respond to their neighbors' movements and positions. These rules determine how group-living animals move, make decisions, and transmit information between individuals. Nonetheless, few studies have explicitly determined these interaction rules in moving groups, and very little is known about the interaction rules of fish. Here, we identify three key rules for the social interactions of mosquitofish (Gambusia holbrooki): (i) Attraction forces are important in maintaining group cohesion, while we find only weak evidence that fish align with their neighbor's orientation; (ii) repulsion is mediated principally by changes in speed; (iii) although the positions and directions of all shoal members are highly correlated, individuals only respond to their single nearest neighbor. The last two of these rules are different from the classical models of collective animal motion, raising new questions about how fish and other animals self-organize on the move.

Keywords
collective animal behavior, fish shoals, group motion, self-propelled particles, self-organization
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-163664 (URN)10.1073/pnas.1109355108 (DOI)000297008900040 ()
Available from: 2011-12-14 Created: 2011-12-13 Last updated: 2017-12-08Bibliographically approved
Baraldi, E., Gregori, G. L. & Perna, A. (2011). Network evolution and the embedding of complex technical solutions: The case of the Leaf House network. Industrial Marketing Management, 40(6), 838-852
Open this publication in new window or tab >>Network evolution and the embedding of complex technical solutions: The case of the Leaf House network
2011 (English)In: Industrial Marketing Management, ISSN 0019-8501, E-ISSN 1873-2062, Vol. 40, no 6, p. 838-852Article in journal (Refereed) Published
Abstract [en]

The purpose of this paper is to investigate the connection between network evolution and technology embedding. To this end, we performed an exploratory case study of the network surrounding an eco-sustainable technology, Leaf House, Italy's first zero-carbon emission house. We apply theories on technological development within industrial networks, with a specific focus on their resource layer and on the three settings involved in embedding an innovation: “developing”, “producing”, and “using”. Our results contribute to these theories by developing four propositions on the connections between network evolution and embedding: first, technology embedding entails both downstream network expansion and upstream restrictions. Secondly, conflicts among actors increase as technology embedding approaches the producing and using settings. Third and fourth, the more the shapes a technology can assume, and the more each of these shapes involves actors acting in different settings, the easier it is to embed it. The paper concludes with managerial implications and suggestions for further research.

Keywords
Industrial networks, Network evolution, Technology embedding, Resources, Eco-sustainability
National Category
Business Administration Engineering and Technology
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-166298 (URN)10.1016/j.indmarman.2011.06.009 (DOI)000295861700004 ()
Available from: 2012-01-11 Created: 2012-01-11 Last updated: 2017-12-08Bibliographically approved
Organisations

Search in DiVA

Show all publications