Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each one is the result of many months of research, so we make a special effort to make our papers clear, inspiring and beautiful, and publish them in leading journals.

  • Date
  • Subject
  • Theme
  • Journal
  • Citations
  • Altmetric
  • SNIP
  • Author
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  • T. FinkT. Fink
  • O. GamayunO. Gamayun
  • A. EsterovA. Esterov
  • Y. HeY. He
  • F. SheldonF. Sheldon
  • A. V. KosyakA. V. Kosyak
  • A. OchirovA. Ochirov
  • E. SobkoE. Sobko
  • M. BurtsevM. Burtsev
  • M. ReevesM. Reeves
  • I. ShkredovI. Shkredov
  • G. CaldarelliG. Caldarelli
  • R. HannamR. Hannam
  • F. CaravelliF. Caravelli
  • A. CoolenA. Coolen
  • O. DahlstenO. Dahlsten
  • A. MozeikaA. Mozeika
  • M. BardosciaM. Bardoscia
  • P. BaruccaP. Barucca
  • M. RowleyM. Rowley
  • I. TeimouriI. Teimouri
  • F. AntenucciF. Antenucci
  • A. ScalaA. Scala
  • R. FarrR. Farr
  • A. ZegaracA. Zegarac
  • S. SebastioS. Sebastio
  • B. BollobásB. Bollobás
  • F. LafondF. Lafond
  • D. FarmerD. Farmer
  • C. PickardC. Pickard
  • T. ReevesT. Reeves
  • J. BlundellJ. Blundell
  • A. GallagherA. Gallagher
  • M. PrzykuckiM. Przykucki
  • P. SmithP. Smith
  • L. PietroneroL. Pietronero
  • Computational capacity of  LRC, memristive and hybrid reservoirs

    Neurocomputing

    FSF. SheldonAKFCF. Caravelli Physical Review E

    Optimal electronic reservoirs

    Balancing memory from linear components with nonlinearities from memristors optimises the computational capacity of electronic reservoirs.

  • Global minimization via classical tunneling assisted by collective force field formation

    Neurocomputing

    FCF. CaravelliFSF. SheldonFL Science Advances

    Breaking classical barriers

    Circuits of memristors, resistors with memory, can exhibit instabilities which allow classical tunnelling through potential energy barriers.

  • Memristive networks: from graph theory to statistical physics

    Neurocomputing

    AZA. ZegaracFCF. Caravelli EPL

    Memristive networks

    A simple solvable model of memristive networks suggests a correspondence between the asymptotic states of memristors and the Ising model.

  • Exactly solvable model of memristive circuits: Lyapunov functional and mean field theory

    Neurocomputing

    FCF. CaravelliPBP. Barucca European Physical Journal B

    Solvable memristive circuits

    Exact solutions for the dynamics of interacting memristors predict whether they relax to higher or lower resistance states given random initialisations.

  • Neurocomputing

    International Journal of Parallel, Emergent and Distributed Systems

    Memristive networks and learning

    Memristive networks preserve memory and have the ability to learn according to analysis of the network’s internal memory dynamics.

  • Neurocomputing

    Physical Review E

    Dynamics of memristors

    Exact equations of motion provide an analytical description of the evolution and relaxation properties of complex memristive circuits.

  • Complex networks

    Physical Review E

    Optimal growth rates

    An extension of the Kelly criterion maximises the growth rate of multiplicative stochastic processes when limited resources are available.

  • Financial markets

    PLoS ONE

    Instability in complex ecosystems

    The community matrix of a complex ecosystem captures the population dynamics of interacting species and transitions to unstable abundances.

  • Percolation theory

    Journal of Statistical Mechanics

    Clusters of neurons

    Percolation theory shows that the formation of giant clusters of neurons relies on a few parameters that could be measured experimentally.

  • Gravity

    Classical and Quantum Gravity

    Cyclic isotropic cosmologies

    In an infinitely bouncing Universe, the scalar field driving the cosmological expansion and contraction carries information between phases.

  • Neurocomputing

    EPL

    From memory to scale-free

    A local model of preferential attachment with short-term memory generates scale-free networks, which can be readily computed by memristors.

SWR