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:

1. 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),
2. understand unbiased approaches to quantitative studies of the neural elements (neurons, dendrites, synapses) using stereological principles,
3. understand usage of neuronal networks and machine learning in data analysis,
4. explain basic principles of synaptic plasticity and associative networks, and their usage for modelling a human brain disease,
5. explain how can genetic predisposition to schizophrenia be analysed using comparative genomics,
6. explain basic principles and possibilities of structural and functional imaging methods and their usage for modelling a human brain disease.

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.