The objectives of "Computational neuroscience" class include advanced computational neural models for learning, architectures and learning methods for dynamical/recurrent neural networks for temporal data and the analysis of their properties, bio-inspired neural modelling, spiking and reservoir computing neural networks, the role of computational neuroscience in real-world applications (by case studies).
The content includes the following topics:
- Computational models of the biological neuron (neuroscience modeling)
- Models of synaptic plasticity and learning (representation/deep learning)
- Recurrent neural networks (dynamical models for temporal data)
- Applications (case-studies)