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Computational Neuroscience and Brain Imaging Techniques for Modeling Brain Functions

Explore the nature of EEG signals using soft computing approaches, focusing on single-cell and network modeling in computational neuroscience. Dive into functional brain imaging methods like EEG, fMRI, MEG, PET, and SPECT to understand brain functions.

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Computational Neuroscience and Brain Imaging Techniques for Modeling Brain Functions

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  1. Systems Science & Informatics: Computational Neuroscience Kaushik Majumdar Indian Statistical Institute Bangalore Center

  2. Neural Information Processing Brain Functions Modeling Computational Neuroscience

  3. Neural Information Processing Functional brain signals: EEG, MEG, fMRI, PET, SPECT Brain Functions Modeling Computational Neuroscience

  4. Neural Information Processing Functional brain imaging: EEG, MEG, fMRI, PET, SPECT Brain Functions Modeling Single cell modeling Network Modeling Population Coding Computational Neuroscience

  5. Neural Information Processing Functional brain imaging: EEG (current) fMRI (future) Brain Functions Modeling Network Modeling Our Focus

  6. Nature of EEG

  7. Human Scalp EEG Processing: Various Soft Computing Approaches Submitted to Applied Soft Computing, Elsevier Fourier Uniformity My Most Recent Works on EEG

  8. Most Recent Papers • Fourier uniformity an useful tool for analyzing EEG signals with an application to source localization, IJCNN’09, Atlanta. http://arxiv.org/ftp/arxiv/papers/0904/0904.4499.pdf • Constraining minimum norm inverse by phase synchronization and signal power of the scalp EEG channels, IEEE Trans. Biomed. Eng., vol. 56(4):1228, Apr 2009.

  9. Epilepsy Research Epilepsy Center of the University Hospital of Freiburg, Germany

  10. Small Network Modeling Neuron Synapse Astrocyte

  11. Enterprising behavior – Actions taken to achieve a reward. Enterprise for Rewards

  12. Two Persons Dynamic Game of Incomplete Information • The person himself/herself is one of the players. The other is his/her environment. • Objectives: • Expectation maximization. • Risk minimization. • Open question – are (1) and (2) mutually independent?

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