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
For Simple Cells, Knowing the Receptive Field is Knowing Spatial Frequency Tuning
As we have seen for the ganglion cells, knowing the shape of the receptive field allows us to infer/predict the spatial frequency tuning of a cell. If you make a map of the receptive fields of these simple cells and you know how many on/off regions they have, their size and sensitivity, you can predict the spatial frequency tuning with an almost perfect match. We can notice that the more complex a cell is in terms of on/off regions the narrower the spatial frequency tuning will be. This shows that not all the cells have the same spatial frequency selectivity and that they "peak" at different frequencies. This, in turn, explains the "whole-individual" perceptual sensitivity to spatial frequencies against contrast sensitivity, that we have seen before.
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