Computational Neuroscience lab

Research group leader

Research focuses

  • Neuronal models of reward-based learning
  • Models of cortical neurons and cortical microcircuitries
  • Models of sensory stimulus representations, attention and perceptual learning


  • Mathematical models
  • Computer simulations

Short description

Our lab uses mathematical models of synapses, neurons and networks to explain aspects of perception and behaviour. In particular, we consider models for the cortical pyramidal neurons and micro-circuitries which are being experimentally investigated in vivo and in vitro at our Institute1. 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.

Further information

Research group’s website