Course curriculum

    1. center-surround receptive fields of retinal ganglion cells (6:52)

    2. Quiz after "center-surround receptive field of retinal ganglion cells"

    3. building an example receptive field by summing weighted Fourier waves (4:09)

    4. quiz after "Building an example receptive field by summing weighted Fourier waves"

    5. mapping the shape of a receptive field, and measuring its contrast sensitivity function (5:40)

    6. quiz after lesson "mapping the shape of a receptive field, and measuring its contrast sensitivity function"

    7. use Fourier transform to link the shape of the receptive field of a neuron with its contrast sensitivity function (7:46)

    8. quiz after lesson "use Fourier transform to link the shape of the receptive field of a neuron with its contrast sensitivity function"

    9. contrast sensitivity function for a difference-of-gaussians receptive field (3:11)

    10. quiz after lesson "contrast sensitivity function for a difference-of-gaussians receptive field"

    1. Information theory: bits and entropy

    2. quiz for "Information theory: bits and entropy"

    3. Information theory: entropy, uncertainty, and surprise

    4. quiz for "Information theory: entropy, uncertainty, and surprise"

    5. information theory: information channel and transmission

    6. quiz for "information theory: information channel and transmission"

    7. Information theory: mutual information

    8. quiz for "Information theory: mutual information"

    9. Entropy and mutual information in gaussian random variables

    10. quiz for "Entropy and mutual information in gaussian random variables"

    11. Information theory: information redundancy

    12. quiz for "Information theory: information redundancy"

    13. Information theory: redundancy, efficiency, and error correction

    14. Survey at the end of chapter "Information theory: a very brief introduction"

    1. Modeling neurons and neural circuits

    1. Correlations and correlation matrices

    2. Eigenvectors, eigenvalues, principal components, and decorrelation

About this course

  • Free
  • 27 lessons
  • 1.5 hours of video content

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