For staging and planning topic to investigated in the field of Computational Neuroscience.
Theoretical
Default Mode Network (DMN)
A brain network active during rest and internal mental processes, including mind-wandering, memory retrieval, and social cognition, and is deactivated during externally focused tasks.
This is not as concrete of a topic, more of a term that loosely describes some associated behavior, so academic sources will never be about just the DMN.
Resources:
Quanta Magazine Video, very generalist, but a good staging ground for more concepts (talks about Memory, Episodic Memory, and many more concepts to cover)
Medlink Technology, not an academic source by any means, but once again a good overview.
Theoretical Neuroscience by Peter Dayan and LF Abbott
This is the textbook that was given to me by a family member, covers a great range of biology, math, and machine learning. Is quite a long book, so I will read it over a long period. Not sure how I will document/note-take for this work, I am thinking of giving it its own folder, and a note on each chapter. If not, then it will be one really long note like a reading log
Theoretical Neuroscience - Peter Dayan and L. F. Abbott
Biology
Hippocampus
This is a very general concept, but I am a little rough on my biology, and the notes I am writing require a strong foundation. A lot of the more complex concepts branch off from notes like this one, so writing a good foundation to base further investigation off of is a good idea.
Computational
Quite similar to theoretical, but I like to think of this as more the math side of things to learn, whereas theoretical covers concepts more specifically atuned to neuroscience, aka theta rhythm.
Differential Math and The Hodgkin-Huxley Model
Will eventually be covered in the textbook, but honing my differential equations skill set would be a wise decision to prepare for this concept. The Hodgkin-huxley model is a bit old, but still widely considered one of the most important unifying theories of brain function.