Carte Blanche


This special online lecture series will take place twice a month on Wednesdays from 10:00 am to 11:00 am EST (4:00 pm – 5:00pm  CET). An unpublished work in progress serves as a basis for this lecture series. A total of six to eight lectures are being planned. Register here!

Each lecture will be 30 minutes long and followed by 30 minutes of Q&A. One week later, a hands-on session will apply knowledge; please bring your own case data! Your favorite programming language can be used (Matlab, Python, R). The code is available in Matlab and in Python (use Python3 with numpy, scipy, and matplotlib). The associated code can be found here.

The Zoom details are:

https://kocun.zoom.us/j/93390785373

Meeting ID: 933 9078 5373
Passcode: 362726

Wednesdays at 10:00 am – 11:00 am EST  (4:00 pm – 5:00pm  CET)

April 13th 2022  (30 min lecture + 30 min questions) 

Fundamental distributions and Bayesian parameters 

Missed the lecture? Enjoy it here.

April 20th 2022 (1h hands-on) 

Putting Bayesian parameters to work 

Missed the lecture? Enjoy it here.

May 4th 2022 (30 min lecture + 30 min questions) 

Fundamentals of Information Theory

Missed the lecture? Enjoy it here.

Entropy of a continuous distribution. 

Available information

Shannon’s Coding Theorem

The Kullback-Leibler Divergence

Temporal Information

The Time Scale Invariance of Temporal Information

Communicated Information 

Contingency

May 11th 2022  (1h hands-on) 

Applying simple Information Theory to associative learning (hands-on)

Missed the lecture? Enjoy it here.

June 1st 2022  

Applications

Missed the lecture? Enjoy it here.

Time-Scale Invariance and Contingency in Reinforcement Learning

Estimating Prospective Contingencies After the First Few Rewards

Estimating Retrospective Contingencies After the First Few Rewards

Measuring The Strength of the Evidence

Applying the nDkl in a Reinforcement Learning Paradigm (aka Operant Conditioning)

Measuring the Strength of the Evidence for Differences in Probability

Checking on the stability of a parameter estimate

Measuring the Growing Strength of Stochastic Stimuli

The Growth of Behavior-Independent Probability Estimates

Tracking the Change in the Behavioral Probabilities

Measuring Contingency Detection Behaviorally and Photometrically

Integrating over the Posterior Distributions

Applying the nDkl to Photometric Data on DA activity.

June 8th 2022  

Continue from previous session & Conclusions