- About our group
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- Computational Neuroscience Projects
- Complex Systems Projects
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- EURESIST - Project
- ICEA - Modelling goal-directed navigation of the rat
- Hippocampal oscillations
- Study of sensory systems
- Software package for complex network analysis
- Dynamics of evolving networks
- A populational model of hippocampus CA3 region slices
- Development of hippocampal place fields
- Hippocampal coding and dynamics
- Location dependent differences between somatic and dendritic IPSPs
- Olfaction and its underlying stochastic phenomena
- The role of self-excitation in the development of topographic order
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- IJCNN 11 Workshop
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- Minisymposium on Computational Aspects of Neurological and Psychatric Diseases
- Workshop on large scale random graphs
- Workshop on Cortico- Hippocampal dynamics: Navigation and Neuromodulation
- Joint Workshop on Neural Autonomous Robots
- Workshop on System Neuroscience
- Neuronhálózatok strukturális kérdései
- 7th Tamagawa Dynamic Brain Forum 2002
- Minisymposium on Computational Neuroscience
- Számítógepes neurológia konferencia, Problemák - Adatok - Modellek
- Budapest - Tampere Minisymposium on Computational Neurolgy
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CNS Course @ BSCS`06
This year Tamás Kiss gives the Computational Neuroscience course at the BSCS. Below are the syllabus of the course and some other downloadable material for the students.
Syllabus
Monday: An Introduction to Computational Neuroscience and the Modelling of Nerve Cells
- General introduction to the subject of Computational Neuroscience and its history. Short introduction to the anatomy and cellular basis of the nervous system.
- Basics of nerve cell electrochemistry and electrophysiology. Conductance based models of neurons.
- Parallel conductance model. Mechanism of action potential generation. The Hodgkin-Huxley model. Ionic currents, ion channels, gate kinetics.
Tuesday: Mathematical Analysis of Neuron Models
- Introduction to differential equations and the theory of dynamical systems.
- Simplified neuron models. Simplifications of the Hodgkin-Huxley model: the FitzHugh-Nagumo-Rinzel model, phase-space analysis. Explanation of bursting by bifurcation analysis. Abstract models: phase model, rate model, McCulloch-Pitts neuron, integrate & fire neuron model
- Beyond the Hodgkin-Huxley model. Diverse voltage- and ligand gated kinetics in single-compartment models. Role of cellular morphology, dendritic effects. What is detailed modeling good for? Taxonomy of neuron models.
Wednesday: Learning in Neural Systems and an Application in Navigation
- Synapses and synaptic plasticity. Detailed, simplified and phenomenological models of synaptic function.
- Cellular bases of learning: synaptic plasticity. The Hebbian rule of learning. Variations for the Hebbian rule. Long term synaptic potentiation and depression. Synaptic plasticity on different time scales. Meta-plasticity. Basics of modelling neural networks. The two (three) levels of neural dynamics. Learning rules: reinforcement, supervised and unsupervised learning. Basic neural architectures: feed-forward and feed-back structures, lateral connections, attractor networks.
- The hippocampus: modelling memory and spatial navigation. Oscillations in memory models. Place cells and place fields. Phase and rate coding. Grid cells. Navigation strategies and some of their models.
Thursday: Computational Neuroscience in Action
- An introduction to the use of some software packages for neural simulations (octave, XPPAut, GENESIS)
Friday: Exams