Advanced Computational Neuroscience
- Understand the principles of information processing in brain circuits and networks
- Gain understanding of the mathematical techniques necessary to develop models of brain dynamics
- Gain skills in developing computational models of learning and neuronal plasticity
- Gain skills and knowledge for modeling motivated behavior
- Gains knowledge and skills in applying mathematical models in neuroscience
- Know basic notions and definitions in computational neuroscience, its connections with other sciences.
- Know the mathematical methods used for the study of the nervous system
- Know the basic modeling techniques for reinforced behavior.
- Be able to distinguish the capacities and restrictions for models considered
- Be able to relate mathematical models to the functioning of the nervous system.
- Know the basic models of neural encoding,
- Know the basic models of network dynamics.
- Possess skills for choosing appropriate computational neuroscience methods for psychological research.
- Possess skills for translation between the various levels of models to describe psychological and physiological levels of interpretation of experimental data
- Know the basic models of neural biophysics
- Basic concepts of reinforcement learningModels of decision making; classical conditioning; operating conditioning, learning by reinforcement; neuroeconomics.
- Models of neural codingSensory processing; linear filters and receptive fields; estimation of receptive fields; edge detectors; Hubel and Wiesel mode of visual processing; natural image statistics, information theory, independent component analysis, neural decoding, encoding by population.
- Models of network dynamicsBiophysics of a neuron, Hudgkin-Huxley formalism, generating action potentials; feedforward and recurrent neural networks; attractors networks; energy functions, Liapunov energy.
- Models of Neuro Biophysics and plasticityLearning and synaptic plasticity; associative memories.
- Homework assignmentsThe homework grade is given based on averaged score for all homework tasks (5 in total) completed by the student. Homework assignments will be given after each lecture and will include written and programming exercises. Assignments are designed to enable grading along correct/incorrect answers to specific questions in each exercise. Total number of questions will be 10. Each correct answer adds one point. The grade is calculated as the proportion of correct answers to the total number of questions. Criteria: 0 – not accepted (Less 5%, or the test was not taken); 1 – very bad (Not less than 5, but less than 15%), 2 – bad (Not less than 15, but less than 25%), 3 – no pass (Not less than 25, but less than 35%), 4 – pass (Not less than 35, but less than 45%), 5 – highly pass (Not less than 45, but less than 55%), 6 – good (Not less than 55, but less than 65%), 7 – very good (Not less than 65, but less than 75%), 8 – almost excellent (Not less than 75, but less than 85%), 9 – excellent (Not less than 85, but less than 95%), 10 – perfect (Not less than 95% and greater).
- Final exam (written)Criteria: 0 – not accepted (No answer), 1 – very bad (No criteria met), 2 – bad (Less then 2 criteria met), 3 – no pass (Less then 3 criteria met), 4 – pass (At least 3 criteria are partially met), 5 – highly pass (At least 3 criteria are met), 6 – good (At least 4 criteria are partially met), 7 – very good (At least 4 criteria are met), 8 – almost excellent (All criteria are met), 9 – excellent (All criteria are met, and at least 3 criteria are fully met), 10 – perfect (All criteria are fully met).
- Interim assessment (2 module)0.4 * Final exam (written) + 0.6 * Homework assignments
- Naldi, G., & Nieus, T. (2018). Mathematical and Theoretical Neuroscience : Cell, Network and Data Analysis. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1737030
- Wiering, M., & Otterlo, M. van. (2012). Reinforcement Learning : State-of-the-Art. Berlin: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=537744
- Pouget, A., Dayan, P., & Zemel, R. (2000). Information Processing with Population Codes. Nature Reviews Neuroscience, 1(2), 125–132. https://doi.org/10.1038/35039062