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  • Conference paper
    Kormushev P, Calinon S, Ugurlu B, Caldwell DGet al., 2012,

    Challenges for the policy representation when applying reinforcement learning in robotics

    , Pages: 1-8
  • Conference paper
    Kormushev P, Ugurlu B, Colasanto L, Tsagarakis NG, Caldwell DGet al., 2012,

    The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans

    , Pages: 3706-3713
  • Journal article
    Calinon S, Kormushev P, Caldwell DG, 2012,

    Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning

    , Robotics and Autonomous Systems
  • Conference paper
    Dickens L, Molly I, Lobo J, Chen P, Russo Aet al., 2012,

    Learning Stochastic Models of Information Flow

    , 28th IEEE International Conference on Data Engineering (ICDE), Publisher: IEEE Computer Society, Pages: 570-581, ISSN: 1063-6382
  • Journal article
    Dallali H, Kormushev P, Li Z, Caldwell DGet al., 2012,

    On Global Optimization of Walking Gaits for the Compliant Humanoid Robot COMAN Using Reinforcement Learning

    , International Journal of Cybernetics and Information Technologies, Vol: 12
  • Conference paper
    Kormushev P, Ugurlu B, Calinon S, Tsagarakis N, Caldwell DGet al., 2011,

    Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Parameterization

    , Pages: 318-324
  • Journal article
    Kormushev P, Nomoto K, Dong F, Hirota Ket al., 2011,

    Time Hopping Technique for Faster Reinforcement Learning in Simulations

    , International Journal of Cybernetics and Information Technologies, Vol: 11, Pages: 42-59
  • Conference paper
    Goodman DFM, Brette R, 2010,

    Learning to localise sounds with spiking neural networks

    To localise the source of a sound, we use location-specific properties of the signals received at the two ears caused by the asymmetric filtering of the original sound by our head and pinnae, the head-related transfer functions (HRTFs). These HRTFs change throughout an organism's lifetime, during development for example, and so the required neural circuitry cannot be entirely hardwired. Since HRTFs are not directly accessible from perceptual experience, they can only be inferred from filtered sounds. We present a spiking neural network model of sound localisation based on extracting location-specific synchrony patterns, and a simple supervised algorithm to learn the mapping between synchrony patterns and locations from a set of example sounds, with no previous knowledge of HRTFs. After learning, our model was able to accurately localise new sounds in both azimuth and elevation, including the difficult task of distinguishing sounds coming from the front and back.

  • Journal article
    Hurault G, Domínguez-Hüttinger E, Langan SM, Williams HC, Tanaka RJet al.,

    Personalised prediction of daily eczema severity scores using a mechanistic machine learning model

    <jats:p>Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control.Objective: We aimed to develop a mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis.Methods: We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Internal and external validation of the predictive model was conducted in a forward-chaining setting.Results: Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment.Conclusion: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level, and could inform the design of personalised treatment strategies that can be tested in future studies. Our model-based approach can be applied to other diseases such as asthma with apparent unpredictability and large variation in symptoms and treatment responses.</jats:p>

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