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9.40 Introduction to Neural Computation (Spring 2018, MIT OCW). Instructor: Prof. Michale Fee. This course introduces quantitative approaches to understanding brain and cognitive functions.
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Description
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. (from ocw.mit.edu)
Course content
- Lecture 01 – Overview and Ionic Currents Unlimited
- Lecture 02 – RC Circuit and Nernst Potential Unlimited
- Lecture 03 – Nernst Potential and Integrate and Fire Models Unlimited
- Lecture 04 – Hodgkin-Huxley Model, Part 1 Unlimited
- Lecture 05 – Hodgkin-Huxley Model, Part 2 Unlimited
- Lecture 06 – Dendrites Unlimited
- Lecture 07 – Synapses Unlimited
- Lecture 08 – Spike Trains Unlimited
- Lecture 09 – Receptive Fields Unlimited
- Lecture 10 – Time Series Unlimited
- Lecture 11 – Spectral Analysis, Part 1 Unlimited
- Lecture 12 – Spectral Analysis, Part 2 Unlimited
- Lecture 13 – Spectral Analysis, Part 3 Unlimited
- Lecture 14 – Rate Models and Perceptrons Unlimited
- Lecture 15 – Matrix Operations Unlimited
- Lecture 16 – Basis Sets Unlimited
- Lecture 17 – Principal Components Analysis Unlimited
- Lecture 18 – Recurrent Networks Unlimited
- Lecture 19 – Neural Integrators Unlimited
- Lecture 20 – Hopfield Networks Unlimited
Instructor
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