|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
Several real-world applications already rely upon modules based on neural paradigms as the computational engines of AI algorithms (e.g., deep/convolutional neural networks). The course aims at overcoming the formal framework of artificial neural networks and to relate more decisively to models derived from Neurosciences. A multidisciplinary approach, which links bidirectionally with the Brain Sciences is crucial: from one side, it fosters the transfer towards artificial systems of the knowledge gained from the study of biological systems (i.e., models specified in hardware, software and eventually in wetware that embody in an essential form their principles, architectures and functionalities), and, from the other side, it demonstrates the usefulness of the “artificial” approach as a method for the investigation of the nervous systems.
Neuromorphic models for the representation and distributed processing of multidimensional signals. Computational primitives and architectural schemes. Applications to the development of perceptual engines to enable autonomous behavior in complex systems and in natural environments.
Through a reverse engineering of the brain, the course aims at presenting and analysing the basic computational paradigms of cortical processing. Specific emphasis is given to how information from the external world is coded, represented and eventually transformed in the cerebral cortex at network level. The neuromorphic solutions of perceptual problems that support visually-guided behaviour are taken as application domain examples and case-studies.
Foundations of neurosensory systems.
Linear algebra and analytical geometry in space.
Elements of signal processing.
Traditional lectures will be supplemented by Journal Club and thematic talks on on-going lab activity (48h). Lab practicals on Spiking Neural Network based vision will be offered on a voluntary basis (limited number of participants).
Part II - Neuromorphic perceptual engines
Slides and other distributed material (available through Aulaweb).
P. Dayan and L.F. Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 2001.
H.A. Mallot. Computational Vision: Information Processing in Perception and Visual Behavior. The MIT Press, 2000.
Office hours: By e-mail appointment. Office: pad. E, Via Opera Pia 13 (3rd floor) Lab: “Bioengineering ”, pad. E, Via Opera Pia 13 (1st floor)
SILVIO PAOLO SABATINI (President)
PAOLO MASSOBRIO (President Substitute)
Oral examination, evaluation of the Journal Club and continuous assessment of active participation during classes.
The student should eventually demonstrate:
The oral discussion is aimed at (1) assessing the level of knowledge on the key concepts presented in the course, and (2) verifying the ability to frame and critically analyze the covered topics.
In general, in addition to the correctness and completeness of the answer, the evaluation criteria comprise: the relevance to the question, the clarity of the answer, and the ability to synthesise.
Journal Club operating methods
|10/02/2022||09:00||GENOVA||Esame su appuntamento|
|15/09/2022||09:00||GENOVA||Esame su appuntamento|