Brain Modelling for Biomedicine and Ict
A.Y. 2024/2025
Learning objectives
Aim of the course are: a) to understand how neuroscientific knowledge and methods can inspire AI methods, and vice versa (physiologically-informed AI models); b) to learn computational techniques for modeling biological neural networks and understanding the brain and its function (in healthy or disease states) through a variety of theoretical constructs and computer science analogies; c) to explore neural information processing at "network level", in developing quantitative models, as well as in formalizing new paradigms of computation and data representation
Expected learning outcomes
Students will learn the principles to build neural models of brain structures, able to embed multi-scale information, from single neuron functional mechanisms to microcircuits, till the generation of high-level functional behaviors. The students are expected to learn how to use large-scale neurocomputational models and brain-inspired neural networks and AI to simulate specific physio-pathological perceptual and motor circuits (e.g. Brain Digital Twin). The students will see also possible ICT applications of these physiologically-informed AI models.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Lesson period
Second semester
Course syllabus
Different neural representations; neural coding/decoding and computation; Neurons: advanced biophysical modeling and computer simulation techniques; Synapse models; Synaptic plasticity, Hebbian learning; biologically plausible neural networks, spiking neural networks ; multi-scale systems of networks; network structure and function; Connectivity and dynamics of neural information processing; information flows in the brain; system neuroscience in perceptual, cognitive and motor tasks (architectures, learning rules and objective functions in the brain); neural correlates of tasks; Brain-Inspired Artificial Intelligence.
Prerequisites for admission
A basic knowledge in "neurophysiology/neuroscience" is required.
A basic knowledge in "programming" and "dynamical systems" is required.
A basic knowledge in "programming" and "dynamical systems" is required.
Teaching methods
The course will be made up of lectures, integrated with seminars, hands-on laboratories with computational tools.
Teaching Resources
- Churchland, P.S. & Sejnowski T.J. 1994. The Computational Brain (Computational Neuroscience Series)
- Dayan, P. & Abbott, L.F. 2005. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
- Rabinovich, M.I. & Friston, K. & Varona, P. 2012. Principles of Brain Dynamics
- Robert Kozma et al; 2018/2023. Artificial Intelligence in the Age of Neural Networks and Brain Computing.
More details will be indicated during the course.
- Dayan, P. & Abbott, L.F. 2005. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
- Rabinovich, M.I. & Friston, K. & Varona, P. 2012. Principles of Brain Dynamics
- Robert Kozma et al; 2018/2023. Artificial Intelligence in the Age of Neural Networks and Brain Computing.
More details will be indicated during the course.
Assessment methods and Criteria
The examination foresees written and oral parts.
BIO/09 - PHYSIOLOGY - University credits: 6
Lessons: 48 hours
Professors:
Benozzo Danilo, Casellato Claudia