Time filter

Source Type

Baldwin J.P.C.,University of York | Hancock Y.,University of York | Hancock Y.,York Center for Complex Systems Analysis
Physica Status Solidi (C) Current Topics in Solid State Physics | Year: 2014

The role of magnetic asymmetric inhomogeneities in zigzag graphene nanoribbons is studied within the context of a generalised tight-binding model with mean-field Hubbard-U interaction. Perturbing the magnetic strength along one edge of the ribbon and adjusting the ribbonwidth are shown to tune the spin-conductance and magnetic properties as the uniaxial strain is increased. We demonstrate the closing of the spin-dependent conductance gap and spin-selective transmission at the Fermi energy for systems with reduced site-specific magnetism along the top-edge of the ribbon. Quantum mechanisms for achieving tunable spin-conductance as a function of strain are revealed as energy minimisation mechanisms in the model. Such mechanisms may be key in the design of future nanoribbon spintronic devices. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dale M.,University of York | Dale M.,York Center for Complex Systems Analysis | Miller J.F.,University of York | Miller J.F.,York Center for Complex Systems Analysis | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Reservoir Computing is a useful general theoretical model for many dynamical systems. Here we show the first steps to applying the reservoir model as a simple computational layer to extract exploitable information from physical substrates consisting of single-walled carbon nanotubes and polymer mixtures. We argue that many physical substrates can be represented and configured into working reservoirs given some pre-training through evolutionary selected input-output mappings and targeted input stimuli. © Springer International Publishing Switzerland 2016.

Fuente L.A.,York Center for Complex Systems Analysis | Fuente L.A.,University of York | Lones M.A.,York Center for Complex Systems Analysis | Lones M.A.,University of York | And 8 more authors.
2013 IEEE Congress on Evolutionary Computation, CEC 2013 | Year: 2013

A novel bio-inspired architecture comprising three layers is introduced for a six-legged robot in order to generate adaptive rhythmic locomotion patterns using environmental information. Taking inspiration from the intracellular signalling processes that decode environmental information, and considering the emergent behaviours that arise from the interaction of multiple signalling pathways, we develop a decentralised robot controller composed of a collection of artificial signalling networks. Crosstalk, a biological signalling mechanism, is used to couple such networks favouring their interaction. We also apply nonlinear oscillators to model gait generators, which induce symmetric and rhythmical locomotion movements. The trajectories are modulated by a coupled artificial signalling network, which yields adaptive and stable robotic locomotive patterns. Gait trajectories are converted into joint angles by means of inverse kinematics. The architecture is implemented in a simulated version of the real robot T-Hex. Our results demonstrate the ability of the architecture to generate adaptive and periodic gaits. © 2013 IEEE.

Bode N.W.F.,York Center for Complex Systems Analysis | Bode N.W.F.,University of York | Wood A.J.,York Center for Complex Systems Analysis | Wood A.J.,University of York | And 2 more authors.
Animal Behaviour | Year: 2011

Many group-living animals show social preferences for relatives, familiar conspecifics or individuals of similar attributes such as size, personality or sex. How such preferences could affect the collective motion of animal groups has been rather unexplored. We present a general model of collective animal motion that includes social connections as preferential reactions between individuals. Our conceptual examples illustrate the possible impact of underlying social networks on the collective motion of animals. Our approach shows that the structure of these networks could influence: (1) the cohesion of groups; (2) the spatial position of individuals within groups; and (3) the hierarchical dynamics within such groups. We argue that the position of individuals within a social network and the social network structure of populations could have important fitness implications for individual animals. Counterintuitive results from our conceptual examples show that social structures can result in unexpected group dynamics. This sharpens our understanding of the way in which collective movement can be interpreted as a result of social interactions. © 2011 The Association for the Study of Animal Behaviour.

Bode N.W.F.,York Center for Complex Systems Analysis | Bode N.W.F.,University of York | Wood A.J.,York Center for Complex Systems Analysis | Wood A.J.,University of York | And 2 more authors.
Behavioral Ecology and Sociobiology | Year: 2011

The theory of collective motion and the study of animal social networks have, each individually, received much attention. Currently, most models of collective motion do not consider social network structure. The implications for considering collective motion and social networks together are likely to be important. Social networks could determine how populations move in, split up into and form separate groups (social networks affecting collective motion). Conversely, collective movement could change the structure of social networks by creating social ties that did not exist previously and maintaining existing ties (collective motion affecting social networks). Thus, there is a need to combine the two areas of research and examine the relationship between network structure and collective motion. Here, we review different modelling approaches that combine social network structures and collective motion. Although many of these models have not been developed with ecology in mind, they present a current context in which a biologically relevant theory can be developed. We argue that future models in ecology should take inspiration from empirical observations and consider different mechanisms of how social preferences could be expressed in collectively moving animal groups. © 2010 Springer-Verlag.

Loading York Center for Complex Systems Analysis collaborators
Loading York Center for Complex Systems Analysis collaborators