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.
- 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)!
- 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".