Activities at the Laboratory of Computational Neuroscience focus on the following questions centered around temporal aspects of information processing in the brain.
![Photo by Fakurian Design on Unsplash](https://www.epfl.ch/labs/lcn/wp-content/uploads/2021/05/fakurian-design-58Z17lnVS4U-unsplash-384x216.jpg)
Networks of (Spiking) Neurons
Standard neural network theory describes the neuron as an input-output unit with a nonlinear transfer function. Real biological neurons are much more complicated than that.
![Photo by krakenimages on Unsplash](https://www.epfl.ch/labs/lcn/wp-content/uploads/2021/06/surprise-2-384x216.jpg)
Learning by Surprise and neoHebbian plasticity rules
Humans and animals learn even in the absence of rewards: e.g., tourists like to explore a new city and children like to explore a new toy. What is the drive for doing this and what is happening in the brain during reward-free learning?
![Photo by Element5 Digital on Unsplash](https://www.epfl.ch/labs/lcn/wp-content/uploads/2021/06/element5-digital-OyCl7Y4y0Bk-unsplash-384x216.jpg)
Reinforcement Learning and the Brain
Humans and animals learn by trial-and-error to repeat rewarded behavior and avoid actions with unpleasant consequences. Reinforcement learning is a computational framework to study this kind of learning.