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26D061NF - Neuromorphic Photonics

Course specification
Course title Neuromorphic Photonics
Acronym 26D061NF
Study programme Electrical Engineering and Computing
Module Nanoelectronics and Photonics
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition
      The goal Introducing students to the basic concepts of neuromorphic and artificial neural networks in the photonic domain and the components used for their implementation. The goal of the course is to involve students in scientific work in this field.
      The outcome Equipping students for independent scientific research in the design and modeling of photonic components for neuromorphic and artificial neural networks, simulating their operation, and experimental characterization of laboratory prototypes.
      Contents
      Contents of lectures Artificial neural networks in the photonic domain. Mechanisms of biological neuron and synapse imitation. Photonic building blocks - an overview of models. Optical perceptrons and spiking neurons. Photonic inhibition and activation. Pulse processing and mechanisms of excitability. Reconfigurable analog photonic networks. Training principles. Photonic reservoir computing. Overview of applications.
      Contents of exercises
      Literature
      1. P.R. Prucnal, B. J. Shastri, "Neuromorphic Photonics“, CRC Press, 2017 (Original title)
      2. M. Gu, E. Goi, Y. Wang, Z. Wan, Y. Dong, Y. Zhang, H. Yu: ”Neuromorphic Photonic Devices and Applications”, SPIE, 2023. (Original title)
      3. H. Suzuki, J. Tanida, M. Hashimoto: ”Photonic Neural Networks with Spatiotemporal Dynamics”, Springer, 2024. (Original title)
      4. Lorenzo De Marinis et al, "Photonic Neural Networks: A Survey" IEEE Access, 2019 (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      8
      Methods of teaching Lectures, mentored research work
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures Test paper
      Practical lessons Oral examination 30
      Projects
      Colloquia
      Seminars 70