Introduction to Computanional Neuroscience

(14 Einträge)

Lecture Introduction to Computational Neuroscience, 1. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 1. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 19. Oktober 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-10-19
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung,
Identifier: UT_20181019_001_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 2. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 2. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 19. Oktober 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-10-19
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Hodgkin-Huxley Theory, K-Channel,
Identifier: UT_20181019_002_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 3. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 3. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 26. Oktober 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-10-26
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Hodgkin-Huxley Theory, Potassium Channel, Sodium Channel, Closed loop,
Identifier: UT_20181026_001_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 4. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 4. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 26. Oktober 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-10-26
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Hodgkin-Huxley Theory, Action Potential, Propagation of AP, Passive Conduction,
Identifier: UT_20181026_002_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 5. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 5. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 02. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-02
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Hodgkin-Huxley Theory, Action Potential, Propagation, Receptive Field,
Identifier: UT_20181102_001_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 6. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 6. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 02. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-02
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Receptive Field, Superposition of point stimuli, Linearity, Correlation, DoG (difference of Gaussians),
Identifier: UT_20181102_002_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 7. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 7. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 09. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-09
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Receptive fields, Lateral Inhibition, Receptive field function, Point spread function, Convolution,
Identifier: UT_20181109_001_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 8. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 8. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 09. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-09
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Convolution, Convolution in time,
Identifier: UT_20181109_002_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 9. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 9. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 16. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-16
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Receptive Fields, Functional Descriptions,
Identifier: UT_20181116_001_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 10. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 10. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 16. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-16
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Flicker, Motion, Spatiotemporal Receptive Field, Spatiotemporal Gabor-function,
Identifier: UT_20181116_002_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 11. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 11. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 23. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-23
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Complex Cells, Energy-Model of Complex Receptive Fields, Motion Detection, Delay-Coincidence-Detector, Correlation-Detector,
Identifier: UT_20181123_001_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 12. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 12. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 23. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-23
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Fourier-Transforms, 2D Images Grating, Michelson Contrast, Modulation transfer function (MTF),
Identifier: UT_20181123_002_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 13. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 13. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 30. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-30
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Fourier-Transforms, sinusoidals, Eigenfunction of convolution, Campbell-curve, Complex numbers,
Identifier: UT_20181130_001_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.

Lecture Introduction to Computational Neuroscience, 14. Lesson

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Title: Lecture Introduction to Computational Neuroscience, 14. Lesson
Description: Vorlesung im WiSe 2018-2019; Freitag, 30. November 2018
Creator: Hanspeter Mallot (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2018-11-30
Subjects: Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Complex numbers, Eigenfunction of convolution,
Identifier: UT_20181130_002_compneuro_0001
Rights: Rechtshinweise
Abstracts: The course will provide an overview over the field of computational neuroscience focussing on four topics: (i) biophysics of excitable membranes: Hodgekin-Huxley theory of the action potential and cable theory of passive conduction, (ii) receptive fields including linear systems and Fourier theory, (iii) neural networks and basics of statistical learning theory, and (iv) neural coding. The focus of the course is on central neuroscience mechanisms; mathematical formalizations are presented on a medium level that should be accessable with highschool or introductory BSc level knowledge of mathematics.