ENS105 The Brain
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Faculty of Engineering and Natural Sciences
Course Objectives
Course objective and description: The goal of this course is to introduce students basic knowledge about brain, its structure and function leading to an understanding of psychology as a biological science. Thus, there is an emphasis on understanding behavior as a product of the brain's physiology and anatomy, determined by the complex interplay of genetic and environmental factors. We will start with foundations such as biological model of behavior, fundamentals of neurons, and functional neuroanatomy. The latter will be fostered by specific neuroanatomy exercises. Then the course will turn its direction to specific topics related to cognition, emotion and mental disorders. There will be special emphasis on the issues including -but not limited to- depression, anxiety, psychopathy and crime which will aid students to explore manifestations of mental state and its health. Also the current neuroscientific perspective for free will will be introduced. Free will had been a topic of philosophy and religion but now modern neurosciences have started to investigate this topic. So in the last part of this course we will investigate some basic neuroscience experiments done in this topic. Then these neuroscience findings will be discussed in more detail together with views from physics, chemistry, biology and philosophy. The course will be a window to ourselves and other people around us and thus, students from diverse disciplines will benefit from this course. No background in psychology or biology is required.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Text book (s): 1. Bear, Connors, Paradiso. (2016). Neuroscience: Exploring the Brain. 4th edition. Wolters Kluwer 2. Pinel, (2007), Biopsychology, 6th edition. Pearson.
Additional Literature
Course literature: Arslan, A. (2015). Genes, brains, and behavior: imaging genetics for neuropsychiatric disorders. The Journal of neuropsychiatry and clinical neurosciences, 27(2), 81–92. https://doi.org/10.1176/appi.neuropsych.13080185 Arslan, A. (2018) Application of neuroimaging in the diagnosis of depression, In: Kim YK (eds). “Understanding Depression- Volume 2. Clinical Manifestations, Diagnosis and Treatment, p69-81 Springer Nature, Singapore. Arslan, A (2018) Mapping the schizophrenia genes by neuroimaging: The promises and the challenges, International Journal of Molecular Sciences. Jan 11;19(1). pii: E219 10.3390/ijms19010219 Aydin O., Aydin, P.U., Arslan A. (2019) Development of neuroimaging-based biomarkers in psychiatry In: Kim YK (eds) Frontiers in Psychiatry. Advances in Experimental Medicine and Biology, 1192:159-195 Springer, Singapore Calhoon, G. G., & Tye, K. M. (2015). Resolving the neural circuits of anxiety. Nature neuroscience, 18(10), 1394–1404. https://doi.org/10.1038/nn.4101 Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., Taylor, A., & Poulton, R. (2002). Role of genotype in the cycle of violence in maltreated children. Science (New York, N.Y.), 297(5582), 851–854. https://doi.org/10.1126/science.1072290 Fergusson, D. M., Boden, J. M., Horwood, L. J., Miller, A. L., & Kennedy, M. A. (2011). MAOA, abuse exposure and antisocial behavior: 30-year longitudinal study. The British journal of psychiatry : the journal of mental science, 198(6), 457–463. Griffith, M (2012) Free Will: The Basics, Routledge, Abingdon and New York Heisenberg, M. (2009) Is free will an illusion?. Nature 459, 164–165. https://doi.org/10.1038/459164a Hills T. (2019). Neurocognitive free will. Proceedings. Biological sciences, 286(1908), 20190510. Kandel E. R. (1998). A new intellectual framework for psychiatry. The American journal of psychiatry, 155(4), 457–469. Libet B,. (1985) Unconscious cerebral initiative and the role of conscious will in voluntary action The Behavioral and Brain Sciences, 8, 529-566 Soon, C., Brass, M., Heinze, HJ. et al. Unconscious determinants of free decisions in the human brain. Nat Neurosci 11, 543–545 (2008). https://doi.org/10.1038/nn.2112 Scientific American Mind https://www.scientificamerican.com/mind/ The brain from top to bottom http://thebrain.mcgill.ca/ The Brain facts Book, Society for Neuroscience. Retrieved in 25.12.2020 http://www.brainfacts.org/book Undergraduate presentation guidelines https://undergraduateresearch.virginia.edu/present-and-publish/presentation-tips https://www.gvsu.edu/ours/oral-presentation-tips-30.htm https://research.usu.edu/undergradresearch/oral-presentations/Teaching Methods
Lecturing
Classroom discussions
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction | Pinel Chapter 1, 2, Bear, Connors Paradiso Chapter 1, lecture notes Bear, Connors Paradiso Chapter 2, 3 |
| 2 | Part I Foundations | Pinel Chapter 1, 2, Bear, Connors Paradiso Chapter 1, lecture notes Bear, Connors Paradiso Chapter 2, 3 |
| 3 | Neuron doctrine and basic neurophysiology | Bear, Connors Paradiso Chapter 2, 3 |
| 4 | Part I Foundations: Neuroanatomy I | Lecture notes and Bear, Connors Paradiso Chapter 7 Appendix |
| 5 | Part I Foundations: Neuroanatomy II | Lecture notes and Bear, Connors Paradiso Chapter 7 Appendix |
| 6 | Part I Foundations: Special focus- Neuroplasticity and hippocampus | Lecture notes and Bear, Connors Paradiso Chapter 23 |
| 7 | Part II Mental disorders | Arslan A., 2015 and lecture notes |
| 8 | Part II Neurobiology of depression | Arslan A., 2018 and lecture notes |
| 9 | Part II Anxious brain | Lecture notes and Calhoon et al., 2015 |
| 10 | Presentation week | |
| 11 | Presentation week | |
| 12 | Part III Free will: Libet's experiment and others | Libet 1985; Soon et al, 2008 |
| 13 | Part III Free will : philosophy, physics and biology | Griffith, 2013 Chapter 1, 2, 3; Heisenberg 2009 |
| 14 | Part III Free will and neuroscience: current state | Hills 2019. |
| 15 | Review |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Participation
AI: Not AllowedAlignment with Learning Outcomes :
In-term exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2
Presentation
AI: Not AllowedAlignment with Learning Outcomes : 1 2
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture Hours
45 hours ⏳ (15 week × 3 h)
Home Study
60 hours ⏳ (15 week × 4 h)
In-term Exam Study
17 hours ⏳ (1 week × 17 h)
Final Exam Study
28 hours ⏳ (2 week × 14 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [ENS105] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1, 2, 3 | Not Allowed | |
| Semester Evaluation Components | |||||
| Participation | 1 | 10 | Not Allowed | ||
| In-term exam | 1 | 35 | 1, 2 | Not Allowed | |
| Presentation | 1 | 15 | 1, 2 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Home Study | 4 | 15 | 60 | |||
| In-term Exam Study | 17 | 1 | 17 | Final Exam Study | 14 | 2 | 28 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||