Welcome and curriculum

This weblog belongs to "An introduction to Statistical Spike Train Analysis Workshop" which is organized by IPM. Koorosh Mirpour will teach the course and administer this weblog. All announcements, assignment and handouts will be available here. A privilege for writing posts in this weblog will be issued for all members of the class. you are welcome to post your comments, points, ideas or even essays about the class or just spike train analysis here.


This is the curriculum of the course:


An introduction to Statistical Spike train analysis

1- Introduction and terminology

A- Computational neuroscience
Spike train analysis and its trends
Statistical spike train analysis
Modeling neural data
Descriptive approaches
Choosing tools: pros and cons of
SPSS
Modeling tools: neuron, genesis …
Programming languages: C++, C#
Matlab and its toolboxes
B- Encoding and decoding and related terminology
C- Neurophysiological data

D- General scheme and goals of spike train analysis workshop
Practical session: introduction to Matlab

2- Spike statistics (1)
Firing rate estimation
Spike count
Spike average and visualization with PSTH

Linear filter and kernel
Half wave Rectification
Practical session: PSTH and Kernel convolution

3- Spike statistics (2)
A- Tuning curve
Estimation
Calculation of parametric change
B- Estimation of response modulation
Significant response
Calculation of onset response
Poisson neuron
C- Normalization
Practical session: finding the significant response

4- Spike statistics (3)
A- Distributions and Poisson process
B- Temporal coding Vs Rate coding
Practical session: distribution estimation and generating a Poisson spike train

5- Correlation and reverse correlation
A- algebra fundamentals of linear regression and fitting
B- correlation and independency of spike trains
C- cross-correlogram and auto-correlogram

D- spike triggered average and receptive field estimation
Practical session: cross and auto correlogram

6- Neural Decoding
A- Introduction to probability and Bayes theorem
B- Neural discrimination
d-prime

7- Information theory (1)
Introduction and basic concepts

8- Information theory (2)
Calculation of information and bias correction
Practical session: mutual information calculation

9- Representational techniques
Principal component analysis
Multi dimensional scaling (MDS)
Clustering
k-mean clustering
Hierarchical clustering
Practical session: clustering neural responses

10- Mixing all together and examples
Practical session:
Comparing neural responses (t-test, ANOVA, MANOVA)
Finding stimulus selectivity profiles
Neural distance (response similarity measurement)