Computational neuroscience – neuroengineering

Course Information
TitleΥπολογιστική νευροεπιστήμη-νευρονική μηχανική / Computational neuroscience – neuroengineering
CodeΒΜ015
FacultyEngineering
SchoolElectrical and Computer Engineering
Cycle / Level1st / Undergraduate, 2nd / Postgraduate
Teaching PeriodSpring
CoordinatorVasileios Kimiskidis
CommonYes
StatusActive
Course ID600020757

Programme of Study: DPMS VIOÏATRIKĪ MĪCΗANIKĪ

Registered students: 7
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses215
Class Information
Academic Year2025 – 2026
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600293502
Type Of Offer
  • Disciplinary Course
Course Type 2021
Specialization / Direction
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • English (Instruction, Examination)
Prerequisites
Required Courses
  • ΒΜ01α Systems biology
  • ΒΜ01β Systems biology
  • ΒΜ04 Biomedical data acquisition and signal processing
General Prerequisites
The class participants are required to have a sufficient level of understanding of the basics of neurophysiology and neuroanatomy as well as basic principles of machine learning and signal analysis. An adequate understanding and proper usage of the relevant concepts and terminology, from a neuroscience and IT point of view, is also required.
Learning Outcomes
Upon completion of this class, participants are expected to have an in-depth understanding of the main areas in the field of computational neuroscience. They will be introduced to methodologies for analyzing and modelling neural signals from the level of single neurons to the system level. Further they will acquire hands-on experience regarding aspects of translational neuroscience including techniques such as non-invasive brain stimulation, brain-computer interfaces and neurofeedback.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work in teams
  • Work in an interdisciplinary team
Course Content (Syllabus)
The scope of the course is to introduce the basic principles of computational neuroscience and familiarize the students with the associated research methodologies. This scientific area lies at the crossroad of neurophysiology/neuroanatomy from the side of medicine and machine learning / signal analysis from the side of information theory. The following topics are introduced in this course: a) from neurons to systems (recording, processing, analysis and modelling of neural signals), b) applications to cognitive and clinical neuroscience: neuroimaging techniques and interpretation of the acquired data, c) brain activity: spectral analysis, nonlinear dynamics, independent component analysis, connectivity analysis, graph-theoretic description, e) examples of translational neuroscience: brain-computer interfaces, neurofeedback, transcranial brain stimulation, neuromimetic intelligence.
Keywords
Bioinformatics, computational modelling, non-invasive brain stimulation, EEG/MEG, Electrophysiology, brain imaging, brain networks, cognitive neuroscience, brain connectivity, neural circuits
Educational Material Types
  • Notes
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Laboratory Teaching
Description
Various methodologies for enhancing the understanding of the topic under study are employed in the course of the laboratories.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures261.0✓
Laboratory Work251✓
Reading Assigment100.4✓
Total612.4
Student Assessment
Description
The final mark is based on a written exam in the form of a quiz including both multiple questions and concise/detailed replies.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Exam with Short Answer Questions (Summative)
  • Written Exam with Extended Answer Questions (Summative)
Last Update
11-03-2024