The course aims at offering knowledge in the field of computational neuroscience. Specifically, the focus will be on modeling multiscale biological neural networks and understanding the brain and its function, through a variety of theoretical constructs and computer science analogies. The multiscale and multidisciplinary approach is the key of challenging international projects, as Human Brain Project and EBRAINS. In view of applications, these neural models represent powerful tools to understand the complex operations underlying perception, actions and memory, in both physiological and pathological states.
- Basic knowledge of the neurobiological principles underlying the brain functioning - Mathematical models to represent the information coding in the brain and the elements of the circuits - Informatic methods and tools to reconstruct, simulate and validate the brain circuit models. - Applications of such models in understanding the complex operations underlying behaviors.
Course Prerequisites
A basic knowledge in “programming” and “mathematics” is required
Teaching Methods
The course will be made up of lectures, integrated with seminars, hands-on informatic laboratories with computational tools.
Assessment Methods
The examination foresees written and oral parts
Texts
Reference books: • Dayan, P. & Abbott, L.F. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. • Churchland, P.S. & Sejnowski T.J. The Computational Brain (Computational Neuroscience Series). More details will be indicated during the course.
Contents
• AI inspired by brain science, how AI and neuroscience drive each other forwards • Computational Neuroscience: approaches and applications • Neural Encoding and Decoding • Neuron and synapse modelling at different levels of detail • Connectivity • Network dynamics • Plasticity and learning • Structure-function in brain circuit models, neural correlates of behaviors • Integration and embodiment
