Department of Physiology

Computational Neuroscience Group

Our lab uses mathematical models of synapses, neurons and networks to explain aspects of perception and behaviour. We consider models for the cortical pyramidal neurons and micro-circuitries which are being experimentally investigated in vivo and in vitro at our Department.1 . A further interest is the neuronal substrate of learning and memory. One question we are addressing is how state-action sequences can be learned from an ongoing stream of synaptic inputs and a single delayed feedback signal2 . We also develop models of sensory processing and its interaction with cortical top-down signals. These explain experimental recordings from the visual cortex obtained while solving perceptual or classification tasks3 , 4 . Our models try to highlight some key mechanisms by which the interaction of synapses and neurons enables our brains to deal with everyday tasks.

Highlights

  • New contribution to NeurIPS: Learning with slow neurons is not a problem anymore!
    This work was selected for an oral presentation at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)!
  • LEnews
    "Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons".
    See Publications - Press Release

  • New contribution to eLife: Evolving to Learn!
  • CRS2021 - Champalimaud Research Symposium - Oct. 15 - Info
  • Talk by Walter Senn "Least action principle and error-based learning in the brain".