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Course Overview and Ionic Currents
Intro to MATLAB and Ionic Currents
RC Circuit and Nernst Potential
Nernst Potential and Integrate and Fire Models
RC Model, Nernst Potential
Hodgkin Huxley Model Part 1
No
Class
Integrate and Fire Model, Hodgkin Huxley Model
Hodgkin Huxley Model Part 2
Dendrites
Synapses
Midterm Review
Review Session
Midterm Exam
Spike Trains
Spike Train Analysis
Receptive Fields
Time Series
Spike Triggered Average, Poisson Process
Spectral Analysis Part 1
Spectral Analysis Part 2
Spectral Analysis
Spectral Analysis Part 3
Midterm 2 Review
Midterm 2 Review
Midterm Exam 2
Help With PSet 5
Rate Models and Perceptrons
Matrix Operations
Perceptons and Matrices
Basis Sets
Principal Components Analysis
Principal Components Analysis
Recurrent Networks
Neural Integrators
Networks
Hopfield Networks
Sequence Generation in Songbirds
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.
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