1. komponenta

Vrsta nastave	Ukupno
Predavanja	30

* Opterećenje je izraženo u školskim satima (1 školski sat = 45 minuta)

COURSE OBJECTIVES:
Students should acquire knowledge and capacity to understand:
1. Basic cell's behaviour that emphasizes its fluidity, plasticity, and stochasticity; key differences between deterministic and probabilistic conception of cellular behaviour; graded vs. stochastic models of gene expression patterns;
2. Basic electrical circuit theory;
3. Neurons as computational devices, sequential and parallel computation in a neural network with examples from visual system; basics in neural network modelling;
4. Basics of how single neurons build functional networks and cognition; elementary attractor dynamics and functional changes of the concept neurons; main differences between short-term memory vs. long-term consolidation;
5. Transmodal hubs: main principles of diffusion tensor imaging (DTI, tractography) and imaging functional connectivity of the human neural networks;
6. Default mode network as a model for studying unconscious brain's functional connectivity; consciousness: fundamental vs. emergent properties;
7. Basic principles of resting state fMRI and independend component analysis of the matrix BOLD signal;
8. Main principles of non-invasive brain stimulation using rTMS and tDCS: modulation of synaptic plasticity for enhancement of cognitive functions and emotional regulation;
9. How to model perceptrons - layered associative networks without synaptic loops; how human visal system can be modeled as a perceptron;
10. Modelling associative networks: supervised vs. unsupervised learning; learning rules in associative neuronal networks: delta rule, backpropagation algorhitm, error-weight relationship, local vs. global minima, adequate training set size, Hebbian vs. non-Hebbian forms of plasticity;
11. Representation of knowledge through semantic networks; how semantic networks are used in artificial intelligence applications;
12. Neurobiological basis of decision making: experimental neuroeconomics and cognitive neuroscience; risk-taking behaviours; fear conditioning; self-confidence, illusion of knowledge; games theory (prisoners' dilemma, Nash equilibrium); rewarding and punishing behavioural reinforcers; multimodal- and reward-value representations in the human prefrontal cortex;
13. Stereological principles for quantitative studies of the neural elements (neurons, dendrites, synapses);
14. Analysis of neuroscience and big data using neuronal networks and machine learning;
15. Basics of main brain diseases, especially neurodegenerative diseases, such as Alzheimer's disease, and psychiatric diseases, such as schizophrenia.

COURSE CONTENT:
1. Introduction to conceptualization of a single nervous cell behaviour and behaviour of nervous cell populations, basic circuit theory, introduction to neural networks and collective dynamics of neuronal populations
2. Foundations of neuronal dynamics at single cell level and neuronal population level
3. Unbiased approaches to quantitative studies of the human brain elements (neurons, dendrites, synapses) using sterological principles - examples of the use of the physical and optical disector, fractionator, and nucleator methods
4. Methods and approaches in data visualisation on selected examples from neuroscience and genetics
5. Deep data analysis using redescription mining - example on analysis of biological and clinical characteristics of cognitive impairment and Alzheimer's disease
6. Examples of data analysis from the field of neuroscience using neuronal networks and machine learning
7. Comparative genomics - example of analysis of genetic predisposition to schizophrenia
8. Modelling a genius savant functional connectivity from imaging data

1. Making Your Own Neural Network., Rashid T, CreateSpace Independent Publishing Platform, Scotts Valley, CA, 2016.
2. Neurostereology: Unbiased Stereology of Neural Systems, Mouton PR, John Wiley & Sons, New Jersey, NJ, 2013.
3. Redescription Mining, Galbrun E, Miettinen P, Springer International Publishing, 2018.
4. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 1st Edition, Wickham H, Grolemund G, O'Reilly Media, Sebastopol, CA, 2017.
5. Data Visualisation with R, 2nd Edition, Rahlf T, Springer, Berlin-Heidelberg-New York, 2019.
6. Foundations of neuronal dynamics (free online resource).
7. Is the cell really the machine? J Theor Biol. 2019; 477: 108-126, Nicholson DJ.
8. Principles of Neural Science, Fifth Edition, Mc Graw Hill, 2012 (electronic, 16 Nov) / 2013 (print); pp. 1525-1532, 1581-1600, 1601-1617, Kandel ER et al. (eds.).
9. http://neuronaldynamics.epfl.ch/online/Ch1.html (Chapter 1 - Introduction: Neurons and Mathematics).
10. https://www.ncbi.nlm.nih.gov/pubmed/24712393 (default-mode network).
11. https://www.ncbi.nlm.nih.gov/pubmed/7622411 (ontogenesis of goal-directed behavior).
12. https://www.ncbi.nlm.nih.gov/pubmed/28123834 (altered PFC networks in schizophrenia).
13. https://www.ncbi.nlm.nih.gov/pubmed/24889330 (mapping human cortical areas).
14. https://www.degruyter.com/view/j/tnsci.2013.4.issue-2/s13380-013-0122-5/s13380-013-0122xml?rskey=aG2aot&result=2&q=functional (functional reorganization of cerebral cortex).
15. https://neuronaldynamics.epfl.ch/online/index.html (chapters other than 1).
16. Volume and number of neurons of the human hippocampal formation in normal aging and Alzheimer's disease. J. Comp. Neurol. 379: 482-494. (Optical disector method), Šimić G et al, 1997.
17. Ultrastructural analysis and TUNEL demonstrate motor neuron apoptosis in Werdnig-Hoffmann disease. J. Neuropathol. Exp. Neurol. 59: 398-407. (Physical disector method), Šimić G et al, 2000.
18. Hemispheric asymmetry, modular variability and age-related changes in the human entorhinal cortex. N, Šimić G et al, 2005.
19. Methods for redescription mining, Phd thesis (https://hal.archives-ouvertes.fr/tel-01399364/document), Galbrun E., University of Helsinki, 2012.
20. Construction and exploration of redescription sets, Phd thesis (http://kt.ijs.si/theses/phd_matej_mihelcic.pdf), Mihelčić M, International Postgraduate School Jožef Stefan, 2018.
21. Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients, Mihelčić M. et al, PLoS One, 2017.
22. The Visual Display of Quantitative Information, Tufte ER, Graphics Press, Cheshire, CT, 2001.

Upis predmeta :
Položen : Bioinformatics 2
Položen : Biology 2
Položen : Introduction to Human Body
Položen : Mechanisms of Human Diseases
Položen : Statistics 2

Obavezni predmet - Redovni Studij - Biomedicinska matematika