Activities at the Laboratory of Computational Neuroscience focus on the following questions centered around temporal aspects of information processing in the brain.
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.
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?
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.