Module Handbook

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Module EIT-ISE-110-M-7

Neurocomputing (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
EIT-ISE-110-M-7 Neurocomputing 4.0 CP (120 h)


CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [EN] English
Module Manager
Area of study [EIT-ISE] Integrated Sensor Systems
Reference course of study [EIT-88.781-SG#2010] M.Sc. Electrical and Computer Engineering [2010]
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
2V+1U EIT-ISE-110-K-7
P 42 h 78 h
ja PL1 4.0 WiSe
  • About [EIT-ISE-110-K-7]: Title: "Neurocomputing"; Presence-Time: 42 h; Self-Study: 78 h
  • About [EIT-ISE-110-K-7]: The study achievement "[PROJ-Schein] proof of successful completion of the project(s)" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: oral examination (30 Min.)
  • Examination Frequency: each semester

Evaluation of grades

The grade of the module examination is also the module grade.


  • Introduction to the field of innovative computer architectures and systems for the technical implementation of biological information processing principles
  • Presentation of diverse aims and solution concepts: Hardware for technical cognition systems, biological-technical interfaces, simulation and verification of models of biological evidence
  • Rehearsal of relevant and commonly applied neural algorithms, including deep networks/deep-learning and analysis of computational requirements and operators
  • Extension from amplitude-coded to spike-coded representation and processing
  • Presentation and effect of potential simplification options for the regarded algorithms
  • Basics of circuit technology (digital, analog, opto-elektronic/optisch) and related implementation technologies (CMOS, WSI, M(O)EMS, etc.) for neural hardware
  • Overview of fundamental architectural principles of neurochips, -processors and -computers
  • Assessment criteria and taxonomy for neural HW
  • Presentation and detailed discussion of selected, representative implementations
  • Outlook on new lines in the field, e.g., evolvable hardware, organic computing, and self-monitoring and repairing sensor systems

Competencies / intended learning achievements

After completing this module you can...
  • ... explain the concepts of dedicated neural and bio-inspired hardware and its application potential and limitations.
  • ... explain the design principles of circuits with alternative signal representation and adaptive structures.
  • ... explain the issues of supervised and unsupervised learning, host-based learning or on-line continuous learning systems.
  • ... explain and master the effects of simplified implementations.
  • ... explain fault-tolerance and robustness issues in conventional and neural computing systems.
  • ... abstract contents/result to M(O)EMS/microsystems application.
  • ... code and/or simulate a neural algorithm in Python, BRIAN, etc.
  • ... compile (labeled) examples from application scenario, create data sets for training/validation/test, select and train a neural network, and apply it for newly measured data (live classification).
  • ... use a neural computing device, e.g., analog or digital neural hardware, customizing it to application and data source, e.g., sensor, and execute real-time classification/processing.
  • ... physically design and validate custom neurons, neural circuits, and neural systems in an established CMOS-process (with given background in microelectronics).

References to Module / Module Number [EIT-ISE-110-M-7]

Course of Study Section Choice/Obligation
[EIT-88.781-SG#2010] M.Sc. Electrical and Computer Engineering [2010] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.A44-SG#2018] M.Sc. Media and Communication Technology [2018] [Free Elective Area] Technical Elective Subjects [W] Elective Module
[EIT-88.D55-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
Module-Pool Name
[EIT-AC-MSC-TW-MPOOL-7] General Elective Modules Master A&C
[EIT-EIT-MSC-TW-MPOOL-7] Technical Elective Modules Master EIT
[EIT-MKT-MSC-TW-MPOOL-7] Technical Elective Modules Master MKT
[EIT-SIAK-DT-ENG-MPOOL] SIAK Certificate "Digital Transformation" - Modules EIT "Engineering"