Teacher: dr. Goran Šimić, professor, MD
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.
Following completion of the course students will be able to:
- explain basic operating concepts in neuroscience (structural and functional nerve cells connectivity, action potential, neuronal networks, imaging functional connectivity of the human neural networks, neuronal dynamics),
- understand unbiased approaches to quantitative studies of the neural elements (neurons, dendrites, synapses) using stereological principles,
- understand usage of neuronal networks and machine learning in data analysis,
- explain basic principles of synaptic plasticity and associative networks, and their usage for modelling a human brain disease,
- explain how can genetic predisposition to schizophrenia be analysed using comparative genomics,
- explain basic principles and possibilities of structural and functional imaging methods and their usage for modelling a human brain disease.
- 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.
- Foundations of neuronal dynamics at single cell level and neuronal population level.
- 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.
- Methods and approaches in data visualisation on selected examples from neuroscience and genetics.
- Deep data analysis using redescription mining – example on analysis of biological and clinical characteristics of cognitive impairment and Alzheimer’s disease.
- Examples of data analysis from the field of neuroscience using neuronal networks and machine learning.
- Comparative genomics – example of analysis of genetic predisposition to schizophrenia.
- Modelling a genius savant functional connectivity from imaging data.