Catalog

Introduction to Neural Computation

Brain and Cognitive Sciences MIT CC BY-NC-SA 4.0 20 lectures

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.

Syllabus

  1. 1 1. Overview and Ionic Currents
  2. 2 2. RC Circuit and Nernst Potential
  3. 3 3. Nernst Potential and Integrate and Fire Models
  4. 4 4. Hodgkin-Huxley Model Part 1
  5. 5 5. Hodgkin-Huxley Model Part 2
  6. 6 6. Dendrites
  7. 7 7. Synapses
  8. 8 8. Spike Trains
  9. 9 9. Receptive Fields
  10. 10 10. Time Series
  11. 11 11. Spectral Analysis Part 1
  12. 12 12. Spectral Analysis Part 2
  13. 13 13. Spectral Analysis Part 3
  14. 14 14. Rate Models and Perceptrons
  15. 15 15. Matrix Operations
  16. 16 16. Basis Sets
  17. 17 17. Principal Components Analysis​
  18. 18 18. Recurrent Networks
  19. 19 19. Neural Integrators
  20. 20 20. Hopfield Networks

Course materials