9.29J | Spring 2004 | Undergraduate

Introduction to Computational Neuroscience

Calendar

Lec # TOPICS KEY DATES
1

Introduction

Examples of Neural Coding, Simple Linear Regression

 
2

Convolution and Correlation 1

Firing Rate

 
 

Optional Lecture 1

Initializing and Using Vectors and Matrices in MATLAB®, Matrix Shortcuts, Plots in MATLAB®, Useful Commands

Simple Statistics and Linear Regression

 
3

Convolution and Correlation 2

Spike-triggered Average

Wiener-Hopf Equations and White Noise Analysis

 
4

Visual Receptive Fields 1

Basics of the Visual System, Center-surround Receptive Fields, Simple and Complex Cortical Cells

Assignment 1 due
 

Optional Lecture 2

Probability Theory

 
5 Visual Receptive Fields 2 Assignment 2 due
 

Optional Lecture 3

Markov Processes

 
6 Operant Matching 1  
7 Operant Matching 2 Assignment 3 due
8 Games 1  
 

Optional Lecture 4

Linear Stability Analysis

 
9 Games 2  
10

Project Meeting 1

Discussion of Topics, Choice of Projects, Work Begins

 
11 Project Meeting 2 Assignment 4 due
12 Project Meeting 3  
13 Project Meeting 4  
14 Project Presentations 1  
15 Project Presentations 2  
16 Ion Channels, Nernst Equation, Passive Electrical Properties of Neurons  
17 The Action Potential, Hodgkin-Huxley Model 1  
18 Hodgkin-Huxley Model 2 Assignment 5 due
19 A-type Potassium Channels, Calcium-Dependent Potassium Channels  
20 Synapses Assignment 6 due
 

Optional Lecture 5

Numerical Methods for Differential Equations

 
21 Associative Memory 1  
22 Associative Memory 2 Assignment 7 due
23 Decisionmaking  
24 Projects  
25 Projects (cont.)  
26 Review  
  Final Exam  

Course Info

As Taught In
Spring 2004
Learning Resource Types
Lecture Notes
Problem Sets