Introduction to Neural Computation

This course introduces quantitative approaches to understanding brain and cognitive functions. Topics include mathematical description of neurons, the response of neurons to sensory stimuli, simple neuronal networks, statistical inference and decision making. It also covers foundational quantitative tools of data analysis in neuroscience: correlation, convolution, spectral analysis, principal components analysis, and mathematical concepts including simple differential equations and linear algebra.

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