This repository was created with the objective of compiling and organizing information from my courses, self-study, and personal interests in signal analysis, computational neuroscience, and theoretical neuroscience.
The main goal of this repository is to provide myself with a condensed and structured version of the material, so I can revisit, review, and re-study these topics more easily over time.
I also hope that these notes and tutorials may be useful for other people who are trying to understand similar topics. However, many explanations are written in a way that is especially intuitive for me. For this reason, some sections may feel more detailed, repetitive, or step-by-step than usual.
These tutorials are designed to help build intuition and understanding from the foundations upward.
Topics such as mathematical concepts, derivations, proofs, signal-processing methods, and computational models are often explained at a beginner-to-intermediate level. The intention is not only to present final formulas or results, but also to make the reasoning behind them easier to follow.
Because of this, some explanations may include:
- repeated definitions,
- intuitive analogies,
- step-by-step mathematical developments,
- simplified examples,
- visual explanations,
- Python implementations,
- and comments written directly in the code.
The objective is to make the material easier to reconstruct and understand when studying it again later.
A large part of this material has been adapted from the course Signal and Data Analysis in Neuroscience, taught by Professor Izhar Bar-Gad and Professor Hadass Tischler.
All rights and credit for the original course material belong to them. I am deeply thankful for their teaching and for the structure of the course, which inspired many of these notes.
Some explanations are also based on concepts from the book:
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
by Peter Dayan and L. F. Abbott
All rights and credit for the original book content belong to the authors and publishers.
Other explanations are based on my own understanding, online resources, and the use of large language models as learning and writing support tools.
These notes are personal study materials and should not be considered a replacement for the original courses, textbooks, or scientific sources.
Although I try to make the explanations clear and accurate, there may be mistakes, simplifications, or interpretations that reflect my own learning process.
Questions, corrections, or feedback are welcome.
You can contact me by email.
I hope this repository is useful.