Duration
25h Th, 20h Pr, 20h Proj.
Number of credits
| Master MSc. in Biomedical Engineering, professional focus | 5 crédits | |||
| Master Msc. in Electrical Engineering, professional focus in Neuromorphic Engineering | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Brains are very bad at counting (i.e., at digital computing) but are excellent at continuously processing sensory stimuli, making decisions about the received sensory information, and putting those decisions into actions in an ever evolving, highly uncertain environment. Decisions are discrete events: each decision happens at a specific time and marks a before and an after it was made. Brains are embodied: their functioning cannot be understood without considering their closed-loop interaction with the body and the environment in which they function.
With the goal of providing the students with the tools needed to study brain computing and translate the gained knowledge into engineered machines, this course introduces the basic mathematical and computational modeling principles to describe and analyze brains and brain-inspired computing architectures as embodied, analog, yet event-based, signal processors and decision makers. We will particularly explore the importance of the mixed nature, that is, simultaneously analog (continuously evolving in time) and digital (event-based), of neuronal systems for understanding brain computing.
It is recommended to take this course together with the Neuromorphic Signal Processing course as the two courses are thought to complement each other.
The course will cover the following table of contents (some themes are optional and will be covered depending on time - optional themes can be developed by students in their final projects):
1. Embodied intelligence, feedback control, and decision-making: an introduction to the central ideas of the course. Presentation of possible final project topics.
2. Flexible signal representation and decision-making in simple feedback systems.
3. Excitable representations and decision-making. Neuronal spikes as decisions.
4. Generalized excitabile decision-making: multi-option spikes.
5. Networked flexible representations, decision-making, and excitability. A network theory of receptive fields.
6. Important types of network structures and distributed decision networks. Neural fields
7. Bursting and (neuro)modulated multi-scale decision-making.
8. Brain-inspired methods for machine-learning and (soft) robotics.
9. Optional themes: biological and artificial learning; analysis of electrophysiological data; searching for brain-inspired computing principles in neuroscience literature.
Learning outcomes of the learning unit
Through theoretical classes and computational exercises developed in the Julia environment, at the end of the course students will:
- Understand the fundamental biological and mathematical principles of brain computing, including elements of learning.
- Be able to design brain-inspired computing primitives using temporal dynamics and bifurcations.
- Use the adquired knowledge in robotics applications and machine-learning.
- Optional: Know the tools to extract brain-computing principles from electrophysiological data.
Prerequisite knowledge and skills
Basic theory of linear and nonlinear dynamical and control systems.
Previous knowledge in neuroscience and computational neuroscience is welcomed.
Good programming skill and a working installation of the latest Julia version: https://julialang.org/
Planned learning activities and teaching methods
The course includes theoretical lectures, exercise sessions (mostly in Julia Notebooks), and project sessions.
For project development, students will be able to access hardware (event-based cameras, TurtleBots, VEX Robot hardware, neuromorpchi chips) and experimental data (Brain-on-Chip setup, in-vivo and in-vtro electrophysiology) from the Department Neuroengineering Lab.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
Course slides and interactive Julia code will be distributed beforehand each lecture.
Suggested preliminary readings:
- Hirsch, Smale, Devaney, DIFFERENTIAL EQUATIONS, DYNAMICAL SYSTEMS, AND AN INTRODUCTION TO CHAOS (Chapters 5,6,8)
- E. Izhikevich - Dynamical Systems in Neuroscience - MIT Press (Chapters 1-7)
- S.H. Strogatz - Nonlinear Dynamics and Chaos - Perseus Books (Chapters 2,3,5-8)
- Leonard, N. E., Bizyaeva, A., & Franci, A. (2024). Fast and flexible multiagent decision-making. Annual Review of Control, Robotics, and Autonomous Systems, 7.
- Sepulchre, R. (2022). Spiking control systems. Proceedings of the IEEE, 110(5), 577-589.
- Sepulchre, R., Drion, G., & Franci, A. (2018). Excitable behaviors. Emerging Applications of Control and Systems Theory: A Festschrift in Honor of Mathukumalli Vidyasagar, 269-280.
Exam(s) in session
Any session
- In-person
written exam AND oral exam
Written work / report
Further information:
Weekly homeworks
Final project
Project oral presentation
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Alessio Franci.
https://sites.google.com/site/francialessioac/