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  • Reinforcement Learning
  • Hebbian Learning
  • Information Theory
  • Homeostasis
  • Clinical Conditions

  • Attention deficit hyperactivity disorder
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  • Computational modelling
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    Edward Young

    University Position
    PhD student


    I come from a background in mathematics; before starting the PhD I completed an undergraduate and masters in mathematics at Cambridge, with a focus on statistics. I now use mathematics to model aspects of brain functioning. My first PhD project focused on providing a normative explanation for homeostasis of neural activity. Using the tools of efficient coding theory, I developed a framework that explains neural homeostasis in terms of a tradeoff between energy and information. Currently I am pursuing two main projects. In the first, we are using reinforcement learning to explore the development of world models in simple recurrent neural network agents. We hope to see how these world models depend on the reward profile of the agent's environment and the agent's sensory apparatus. In the second, we are using the methods of dynamic systems theory to explore the behaviour of Hebbian learning in recurrently connected populations of neurons. Outside of my research I run two discussion groups. The Theoretical Neuroscience Reading Group meets on a weekly basis to work through a technical paper or textbook chapter. Everyone is welcome, regardless of their background in mathematics or biology. Luminomelia Cambridge is a philosophy and science discussion group that meets on a weekly basis to listen to a short talk and discuss the implications of scientific research. This is open to everyone. I am also involved in CU Students Against Pseudoscience. If you are interested in collaborating then please get in touch at ey245[at]cam[dot]ac[dot]uk

    Key Publications