Calendar
The optional lectures listed below are for students who are less familiar with MATLAB®. These lectures are designed to teach students basic skills in some specific uses of the program that are required to complete the homework in this class.
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LEC # |
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TOPICS |
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Lecture 1 |
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- Introduction.
- Examples of neural coding. Simple linear regression.
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Lecture 2 |
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- Convolution, correlation. Firing rate. Spike-triggered average.
- Wiener-Hopf equations and white noise analysis.
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Optional Lecture 1 |
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- Initializing and using matrices in MATLAB®. Linear modelling in a sample data set.
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Lecture 3 |
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- More about convolution and correlation.
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Optional Lecture 2 |
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- Basic linear algebra in MATLAB®. Vector and matrix addition and multiplication. Solving sets of linear equations using matrices.
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Lecture 4 |
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- Visual receptive fields I.
- Basics of the visual system. Center-surround receptive fields. Difference of Gaussians model.
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Lecture 5 |
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- Visual receptive fields II.
- Simple cortical cells, separable and nonseparable.
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Lecture 6 |
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Lecture 7 |
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- Fourier series. Pure tones. Perception of periodic complex tones.
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Lecture 8 |
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- Fourier transform. Spectral analysis. The cochlea as a Fourier analyzer.
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Lecture 9 |
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- Features and filters in vision.
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Lecture 10 |
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- Probability theory and Bernoulli processes.
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Lecture 11 |
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- Poisson processes and spike train statistics.
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Lecture 12 |
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Lecture 13 |
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Lecture 14 |
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- Ion channels. Nernst equation. Passive electrical properties of neurons.
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Lecture 15 |
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- The action potential. Hodgkin-Huxley model.
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Lecture 16 |
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- Hodgkin-Huxley model. Numerical methods for differential equations.
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Lecture 17 |
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- A-type potassium channels, calcium-dependent potassium channels.
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Lecture 18 |
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- Phase plane analysis of the Morris-Lecar model.
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Lecture 19 |
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Lecture 20 |
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Lecture 21 |
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- Ion channels and Markov processes.
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Lecture 22 |
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- Diffusion and calcium dynamics.
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Lecture 23 |
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Lecture 24 |
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- Synaptic plasticity, Long-term potentiation.
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