Technische Universität Ilmenau

Neuromorphic Engineering 1 - Interactive curriculae of TU Ilmenau

The interactive curriculae provide information on the degree programmes offered by the TU Ilmenau.

Please refer to the respective study and examination rules and regulations for the legally binding curricula (Annex Curriculum).

You can find all details on planned lectures and classes in the course catalogue.

Please note that this page is no longer updated. All modules and study plans from PO version 2021 onwards (Bachelor and Master study programs) are now available on the Campus Portal.

module properties Neuromorphic Engineering 1 in degree program Master Micro- and Nanotechnologies 2021
module number200641
examination number2101016
departmentDepartment of Electrical Engineering and Information Technology
ID of group 2143 (Micro- and Nanoelectronic Systems)
module leader Dr. Frank Schwierz
term summer term only
languageEnglisch
credit points5
on-campus program (h)45
self-study (h)105
obligationelective module
examwritten examination performance, 90 minutes
details of the certificate
link to Moodle course
teacher
signup details for alternative examinations
maximum number of participants
previous knowledge and experience
learning outcome

After the lectures and exercises, the students are able to understand and analyze the principles of neural computation methods so that they can compare various advantages and disadvantages of neuro-inspired networks.

content
  • Biophysical background: neurons, synapses, Data processing invertebrates and vertebrates, Implicit and explicit learning, Short and long-term potentiation, Plasticity
  • Spiking neurons models
  • Hebbian learning theory
  • Neural Networks: an overview (McCulloch-Pitts Neuron, Perceptron, Adalein/Madaline, ART, Boltzmann-Machine)
  • Neuronal analog circuits: Axon Hillock Circuit, LIF-Neuron, STDP, AER

media of instruction and technical requirements for education and examination in case of online participation

PowerPoint presentation, blackboard

literature / references
  • Gerstner and Kistler, Spiking Neuron Models. Single Neurons, Populations, Plasticity, Cambridge University Press, 2002
  • Analog VLSI and Neural Systems, C. Mead, Addison-Wesley Pub. Comp. 1989
evaluation of teaching