Introduction to Computanional Neuroscience
(25 Einträge)
Lecture Introduction to Computational Neuroscience, 1. Lesson
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
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
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
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
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
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
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
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
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
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
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
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
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
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. |
Lecture Introduction to Computational Neuroscience, 15. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 15. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 07. Dezember 2018 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2018-12-07 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Fourier-Transforms, Fourier-decomposition, box-car function, General periodic function, lowpass-filter, |
Identifier: | UT_20181207_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, 16. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 16. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 07. Dezember 2018 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2018-12-07 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Coefficients, Orthogonality of sinusoidals, Relaxing periodicity constraint, Fourier-forward-transform, Fourier-backward-transform, |
Identifier: | UT_20181207_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, 17. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 17. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 14. Dezember 2018 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2018-12-14 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Artificial Neural Networks, Activation dynamics, Weight dynamics, Learning rules, |
Identifier: | UT_20181214_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, 18. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 18. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 14. Dezember 2018 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2018-12-14 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Artificial Neural Networks, Perceptron, |
Identifier: | UT_20181214_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, 19. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 19. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 21. Dezember 2018 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2018-12-21 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Perceptron, X-OR-Problem, Parity-Problem, Perceptron Learning Rule, Error Minimization, |
Identifier: | UT_20181221_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, 20. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 20. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 21. Dezember 2018 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2018-12-21 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Neural Networks, Perceptron, |
Identifier: | UT_20181221_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, 21. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 21. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 11. Januar 2019 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2019-01-11 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Perceptron, Associator, |
Identifier: | UT_20190111_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, 22. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 22. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 11. Januar 2019 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2019-01-11 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Associator, Self-organization, Competitive learning, Hebb learning rule, |
Identifier: | UT_20190111_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, 23. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 23. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 25. Januar 2019 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2019-01-25 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Competitive Learning, Competition between synapses, Oja learning rule, |
Identifier: | UT_20190125_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, 24. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 24. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 25. Januar 2019 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2019-01-25 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Competitive Learning, Competition between neurons, Self-organizing feature map, Kohonen-Map, Coding, |
Identifier: | UT_20190125_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, 25. Lesson
Title: | Lecture Introduction to Computational Neuroscience, 25. Lesson |
Description: | Vorlesung im WiSe 2018-2019; Freitag, 01. Februar 2019 |
Creator: | Hanspeter Mallot (author) |
Contributor: | ZDV Universität Tübingen (producer) |
Publisher: | ZDV Universität Tübingen |
Date Created: | 2019-02-01 |
Subjects: | Neurobiologie, Computational Neuroscience, Lecture, Vorlesung, Coding, Tuning Curve, Shannon-Weaver Information Theory, |
Identifier: | UT_20190201_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. |