Computational neuroscience provides insight into the development and function of neural systems at many different structural scales, from biophysical to circuit and integrated systems levels. This is done using methods that include theoretical analysis and modelling of neurons, networks and brain systems. These methods are complemented by empirical techniques in neuroscience. The study of computational mechanisms in neurons, the analysis of signal processing in neural circuits, the representation of sensory information, the study of models of sensory and motor integration systems, and models of learning and memory are addressed. Finally, neurocomputing at the frontier of science and engineering will be addressed, integrating representation models and control theory. Course content. 1. Neuronal and action potential model (spikes). 2. Analysis of spike series: visualisation and statistical description. 3. Connections between neurons and neural networks. 4. Conductance-based models. 5. Frequency coupling in LFPs. 6. Dynamics of neural networks. 7. Synaptic plasticity. 8. System dynamics. 9. Analysis techniques: PCA, Hidden Markov, etc.

Classroom lectures. Face-to-face classes: reduced (workshops, seminars, case studies). Student individual work. Laboratory session. Final exam. Seminars and lectures supported by computer and audiovisual aids. Practical learning based on cases and problems, and exercise resolution. Individual and group or cooperative work with the option of oral or written presentation. Individual and group tutorials to resolve doubts and queries about the subject. Internships and directed laboratory activities.