Notes
A collection of fragments of understanding in the pursuit of deeper questions.
Contents
- Computational Vision
- Introduction to Data Science
- Translational Neuromodeling
- Deep Learning in Artificial & Biological Neuronal Networks
- Introduction to Neuroinformatics
- Intro to Neuroscience
- Systems Neuroscience
- Readings in Neuroinformatics
- Neurobiology of Consciousness
- Econometrics
- Principles of Finance
- Big Data & Databases
- Game Theory
- Intro to Blockchain
- Marketing Analytics
- Financial Accounting
- Fundamentals of Computer Science
- Foundamentals of Information Technology Law
- Statistics
- Computer Programming
- Macroeconomics
- EU Information Law & Data Economy
- Advanced Mathematics & Statistics
- Principles of Management
- Mathematics
- Microeconomics
Sejnowski's Infomax Network (ICA) Rule & Bienenstock-Cooper-Monroe (BCM) Rule
Sejnowski's Infomax Network (ICA) Rule Paper: "An Information-Maximisation Approach to Blind Separation and Blind Deconvolution".
The Infomax rule utilizes a nonlinear function and yields a method for implementing Independent Component Analysis (ICA).
Bienenstock-Cooper-Monroe Rule
Activity is measured by y along the horizontal axis, and with no activity no change in weights takes place. Activity below a certain threshold triggers the LTD regime, and activity above this threshold triggers the LTP regime. Measuring neuronal output across a certain window indicates that there is a biological basis for this. Above some threshold, the weight updates increased as the stimulation frequency was increased. This was tested experimentally in the hippocampus and primary visual cortex, stimulating inputs to a neuron and measuring its spiking frequency. In this experiment, below a stimulation frequency of about 10Hz the weight updates were negative, while above this stimulation frequency they were positive.
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