Neuroinformatics or Computational Neuroscience is an emerging and dynamically developing field aiming to elucidate the principles of information processing by the nervous systems as well as applying information technology to the processing of neuroscientific data. This course aims to develop and apply computational methods for studying brain and behavior as well as understanding the dynamics of the conscious mind. The course will cover: Introduction to Neuroinformatics; basic neurobiology: from the brain to single neurons; biophysics of single neurons; synapses; dendrites and axons. Conductance-based neuron models: the generation of action potentials and the Hodgkin and Huxley equations. Dendritic trees, the propagation of action potentials, cable theory, compartmental models. Modelling synapses. Spiking neuron models and response variability: leaky integrator and integrate-and-fire type neuron models, spike time variability. Current topics in Computational Neuroscience including (a) understanding of the neural code (b) Synaptic Plasticity. Bottom-up/top-down modeling of the brain: modeling of self-control behavior as an example of top-down modeling. Modelling consciousness. Applications of Neuroinformatics; Neuroinformatics vs Bioinformatics.