2021/2022

2023/2024

DM270

NN (INDEFINITO/INTERDISCIPLINARE)

DEPARTMENT OF MATHEMATICS "FELICE CASORATI"

ARTIFICIAL INTELLIGENCE

PERCORSO COMUNE

3°

2nd semester (04/03/2024 - 18/06/2024)

3

36 lesson hours

English

ORAL TEST

FARIS PAWAN SIRWAN FARIS (titolare) - 1.5 ECTS
PISCHEDDA DORIS - 1.5 ECTS

Basic knowledge in neurophysiology and some familiarity with programming.

This laboratory aims at understanding the core concepts of cellular and circuit signals in neuroscience, covering neuron anatomy, physiology, and synaptic transmission. It is also aimed to provide practical skills essential to understand the principles of neural bases of multi-scale information from mechanisms of single neurons and synapses to functional microcircuits with specific connectivity and plasticity, till the generation of high-level function behaviors. In particular, acquiring experience with several recording techniques and analyzing brain data with standard pipelines as well as novel AI methods will provide a hint on how neural data are collected, analyzed, and interpreted to investigate brain function.

With this course, students will acquire proficiency in various recording techniques for cellular and circuit signals, as well as neural architectures, including electrophysiology, single-unit recordings, multi-electrode arrays, patch-clamp recording, and large-scale neural networks, shedding light on both physiologic and pathologic examples. Moreover, employing data analysis methods to interpret neural data effectively and exploring the integration of cellular and circuit signal insights in AI, and highlighting its potentials and challenges, will provide a critical perspective on the integration of AI tools in neuroscience.

Cellular and circuit signals:
- Introduction (anatomy and physiology of neurons, neural circuits, neural firing, synaptic transmission, and plasticity).
- Recording techniques for cells and circuits (electrophysiology, single-unit recordings, multi-electrode arrays, and patch-clamp recording, imaging techniques)
- Data analysis methods, and applications to AI. Some examples will deal especially with the cerebellum circuit.

Ensemble brain signals:
- Introduction
- Recording techniques
- Analysis methods
- Example analyses of human fMRI recordings.

Brain-inspired systems:
- Bringing together artificial intelligence and neuroscience
- Intro to Neuro-AI and related methods
- Machine learning applied to neural data (hands-on)
- Examples of brain-inspired technologies.

This laboratory employs a variety of teaching methods, including traditional lectures to build foundational knowledge, hands-on laboratory sessions for practical skills development, AI demonstrations, seminars that encourage group discussions on scientific journal papers and, possibly, guest lectures by post-doctoral researchers experienced in the field.

Digital material will be provided through the course page. This includes scientific papers and relevant books. A few examples are:
- Hebart, Martin N., Kai Görgen, and John-Dylan Haynes. "The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data." Frontiers in neuroinformatics 8 (2015): 88.
- Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S., Hudspeth, A. J., & Mack, S. (Eds.). (2000). Principles of neural science (Vol. 4, pp. 1227-1246). New York, NY, US: McGraw-hill.
- Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Preprocessing fMRI data. In R. A. Poldrack, J. A. Mumford and T. E. Nichols (Eds.), Handbook of functional MRI data analysis (pp. 34-52). New York, NY, US: Cambridge University Press.
- Purves, D., Augustine, G. J., Fitzpatrick, D., Hall, W., LaMantia, A. S., & White, L. (2019). Neurosciences. De Boeck Supérieur.
- Wudarczyk, O. A., Kirtay, M., Kuhlen, A. K., Abdel Rahman, R., Haynes, J. D., Hafner, V. V., & Pischedda, D. (2021). Bringing Together Robotics, Neuroscience, and Psychology: Lessons Learned From an Interdisciplinary Project. Frontiers in Human Neuroscience, 15, 160.

The examination includes written and oral parts. Practical exercises may also be evaluated.
The subject of the exam is the contents of the lectures, hands-on sessions, and educational seminars.

The slides of the lectures and the other course materials will be made available through the download area of the course website.