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17: Principal Components Analysis_ - Intro to Neural Computation
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Classroom Contents
Introduction to Neural Computation
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- 1 1: Course Overview and Ionic Currents - Intro to Neural Computation
- 2 2: Resistor Capacitor Circuit and Nernst Potential - Intro to Neural Computation
- 3 3: Resistor Capacitor Neuron Model - Intro to Neural Computation
- 4 4: Hodgkin-Huxley Model Part 1 - Intro to Neural Computation
- 5 5: Hodgkin-Huxley Model Part 2 - Intro to Neural Computation
- 6 6: Dendrites - Intro to Neural Computation
- 7 7: Synapses - Intro to Neural Computation
- 8 8: Spike Trains - Intro to Neural Computation
- 9 9: Receptive Fields - Intro to Neural Computation
- 10 10: Time Series - Intro to Neural Computation
- 11 11: Spectral Analysis Part 1 - Intro to Neural Computation
- 12 12: Spectral Analysis Part 2 - Intro to Neural Computation
- 13 13: Spectral Analysis Part 3 - Intro to Neural Computation
- 14 14: Rate Models and Perceptrons - Intro to Neural Computation
- 15 15: Matrix Operations - Intro to Neural Computation
- 16 16: Basic Sets - Intro to Neural Computation
- 17 17: Principal Components Analysis_ - Intro to Neural Computation
- 18 18: Recurrent Networks - Intro to Neural Computation
- 19 19: Neural Integrators - Intro to Neural Computation
- 20 20: Hopfield Networks - Intro to Neural Computation
