Lecture notes

Lecture slides on statistical modeling
Histogram Optimization | Kernel Optimization | Dynamic Interactions

- 0 Introduction
- 1 Poisson process
- 2 Renewal / Non-Poisson
- 3 point process-GLM
- 4 Rate estimation
- 5 State-space
- Histogram Optimization
- Kernel Optimization
- State-space analysis
- Point process
- HOME Page

Lecture contents

Hypothesis test

  • Introduction, t-test, multiple comparison correction
  • ANOVA, 2-way ANOVA
  • Effect size, power analysis

Machine learning lectures.

  • Principal component analysis
  • Boltzmann machine (plan to up)
  • Hierarchical models (plan to up)

Point process theory



Hypothesis test



Principal component analysis

Lectures on principal component analysis

RIKEN Brain Scinece Insitute

Kyoto University


Point process theory

Lecture 0: Course introduction



Lecture 1: A Poisson point process



Lecture 2: Renewal and non-Poisson processes



Lecture 3: Point process-GLM (Encoding)



Lecture 4: Inference for a Poisson process: Spike-rate estimation



Lecture 5: State-space model and a point process filter (Decoding)


Lectures on point process thery

2017 Mar 4 Brain wakate


2016 Feb 10 Introduction to the point process network

A photo cited from the lecture page of Institute of Statistics and Mathematics. http://www.ism.ac.jp/lectures/27n.html


2013 Mar 3-12 Introduction to statistical models of neural spike train data [link]

Two-weeks course at the Institute for Research in Fundamental Sciences, School of Cognitive Sciences