ID:
509530
Durata (ore):
36
CFU:
3
SSD:
Indefinito/Interdisciplinare
Anno:
2024
Dati Generali
Periodo di attività
Secondo Semestre (03/03/2025 - 13/06/2025)
Syllabus
Obiettivi Formativi
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, microscopy, behavioral tests, 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 allow the students to develop a critical perspective on the integration of AI tools in neuroscience.
With this course, students will acquire proficiency in various recording techniques for cellular and circuit signals, as well as neural architectures, including electrophysiology, microscopy, behavioral tests, 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 allow the students to develop a critical perspective on the integration of AI tools in neuroscience.
Prerequisiti
Basic knowledge in neurophysiology and some familiarity with programming.
Metodi didattici
This laboratory employs multiple teaching methods, including traditional lectures to build foundational knowledge, hands-on laboratory sessions for practical skills development, and AI demonstrations.
Verifica Apprendimento
The examination will be an individual oral exam, where both Professors will ask questions. Students who attended the hands-on sessions have the possibility to replace part of the oral exam with a practical project.
The subject of the exam is the contents of the lectures, and the hands-on sessions.
The subject of the exam is the contents of the lectures, and the hands-on sessions.
Testi
Digital material will be provided through the course page. In addition, scientific papers and relevant books will be used. Some examples are:
- Baraka, K., Alves-Oliveira, P., & Ribeiro, T. (2020). An Extended Framework for Characterizing Social Robots. In C. Jost, B. Le Pévédic, T. Belpaeme, C. Bethel, D. Chrysostomou, N. Crook, M. Grandgeorge, & N. Mirnig, Human-Robot Interaction Evaluation Methods and Their Standardization (Vol. 12). Springer, Cham.
- Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., & Šabanović, S. (2020). Research methods. In C. Bartneck, T. Belpaeme, F. Eyssel, T. Kanda, M. Keijsers, & S. Šabanović, Human-Robot Interaction: An Introduction (1st ed., pp. 126–160). Cambridge University Press.
- Carter, M., & Shieh, J. C. (2015). Guide to research techniques in neuroscience. Academic Press.
- 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). Handbook of functional MRI data analysis. 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.
- Baraka, K., Alves-Oliveira, P., & Ribeiro, T. (2020). An Extended Framework for Characterizing Social Robots. In C. Jost, B. Le Pévédic, T. Belpaeme, C. Bethel, D. Chrysostomou, N. Crook, M. Grandgeorge, & N. Mirnig, Human-Robot Interaction Evaluation Methods and Their Standardization (Vol. 12). Springer, Cham.
- Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., & Šabanović, S. (2020). Research methods. In C. Bartneck, T. Belpaeme, F. Eyssel, T. Kanda, M. Keijsers, & S. Šabanović, Human-Robot Interaction: An Introduction (1st ed., pp. 126–160). Cambridge University Press.
- Carter, M., & Shieh, J. C. (2015). Guide to research techniques in neuroscience. Academic Press.
- 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). Handbook of functional MRI data analysis. 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.
Contenuti
Sub-cellular, cellular, and circuit signals:
- Anatomy and physiology of neurons, at sub-cellular, cellular, and circuit level, alongside a deep insight into neural firing, synaptic transmission, and plasticity
- Membrane proteins (Ion channels and Receptors), cell signaling, pathways, and physiological processes
- Recording techniques (electrophysiology, imaging, molecular and behavioral techniques) and data analysis methods and applications to AI.
Ensemble brain signals:
- Large-scale networks and multiscale modeling
- Non-invasive recording techniques
- Analysis methods (mass-univariate analysis): preprocessing, first-level analysis, and group analysis
- Example analyses of human fMRI recordings (hands on).
Brain-inspired systems:
- General introduction to AI (interdisciplinary perspective) and social robotics
- Bringing together different AI disciplines
- Intro to Neuro-AI and related methods
- AI applied to neural data (hands-on)
- Examples of brain-inspired technologies.
- Anatomy and physiology of neurons, at sub-cellular, cellular, and circuit level, alongside a deep insight into neural firing, synaptic transmission, and plasticity
- Membrane proteins (Ion channels and Receptors), cell signaling, pathways, and physiological processes
- Recording techniques (electrophysiology, imaging, molecular and behavioral techniques) and data analysis methods and applications to AI.
Ensemble brain signals:
- Large-scale networks and multiscale modeling
- Non-invasive recording techniques
- Analysis methods (mass-univariate analysis): preprocessing, first-level analysis, and group analysis
- Example analyses of human fMRI recordings (hands on).
Brain-inspired systems:
- General introduction to AI (interdisciplinary perspective) and social robotics
- Bringing together different AI disciplines
- Intro to Neuro-AI and related methods
- AI applied to neural data (hands-on)
- Examples of brain-inspired technologies.
Lingua Insegnamento
INGLESE
Altre informazioni
The slides of the lectures and the other course materials will be made available through the download area of the course website.
Corsi
Corsi
ARTIFICIAL INTELLIGENCE
Laurea
3 anni
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