Neuroscience
Resource lists for computational and cognitive neuroscience, from neuromorphic hardware to evolutionary computation: courses, papers, and books.
On this page
Neuromorphic Computing
- Explore and contribute to Jaxley
- Chris Eliasmith's lab
- Elisa Sandamirskaya and jobs
General Resources
Cognitive Neuroscience - SA
Computational Neurotechnology
Comprehensive Resource List for Biologically Inspired AI & Neuroscience
An exhaustive list of books, courses, and research papers on computational neuroscience, neuromorphic computing, biologically inspired AI, cognitive science, and evolutionary computation.
For biology, see Biology.
1. Computational Neuroscience
How the brain computes & learns at the neuronal level.
Books
- Principles of Neural Science – Eric Kandel, James Schwartz, Thomas Jessell
- Theoretical Neuroscience – Peter Dayan & Larry Abbott
- Dynamical Systems in Neuroscience – Eugene Izhikevich
Courses
- Computational Neuroscience – Coursera
- The Brain and Space – Neuromatch Academy
- MIT 9.40: Introduction to Neural Computation
Research Papers
- Dayan & Abbott (2001) - Theoretical Neuroscience
- Izhikevich, E. M. (2003) - Simple model of spiking neurons
2. Biologically Inspired AI (Bio-AI)
Brain-inspired learning algorithms & alternative learning methods beyond backpropagation.
Books
- How to Build a Brain – Chris Eliasmith
- The Self-Assembling Brain – Peter Robin Hiesinger
- The Free Energy Principle – Karl Friston
Courses
Research Papers
- Friston, K. (2010) - The free-energy principle: A unified brain theory?
- Eliasmith, C. (2013) - A Large-Scale Model of the Brain
3. Neuromorphic Computing & Engineering
Building hardware that mimics brain function.
Books
- Neuromorphic Engineering – Kwabena Boahen
- Introduction to Neuromorphic Computing – Ranjan K. Mallik
Courses
- Neuromorphic Computing – Coursera (Delft University)
- Neuromorphic Systems & Algorithms – Stanford
Research Papers
4. Evolutionary Computation & Artificial Life
AI that evolves rather than learns via backpropagation.
Books
- Artificial Life – Christopher Adami
- Evolutionary Computation – David B. Fogel
Courses
- Evolutionary Computation – Coursera
- Artificial Life – MIT Press
Research Papers
- Stanley, K. O. & Miikkulainen, R. (2002) - Evolving Neural Networks
- Holland, J. H. (1992) - Genetic Algorithms
5. Cognitive Science & Computational Cognition
Understanding human-like reasoning & perception for AI.
Books
- How to Create a Mind – Ray Kurzweil
- Mind as Machine – Margaret Boden
Courses
- Computational Cognitive Science – MIT
- Cognitive Science – Coursera
Research Papers
6. Theoretical Neuroscience & Complex Systems
Mathematical foundations of intelligence & brain computation.
Books
- Neural Networks and Brain Function – Rolls & Deco
- Information Theory and Neural Coding – Rieke et al.
Courses
- Mathematical Modeling of Cognition – MIT
- Neural Computation – edX
Research Papers
Neuromodulators
Quick reference for understanding and managing brain chemistry day-to-day.
Targeting Guide
Dopamine (Motivation, Goal-Seeking)
Stimulate:
- Morning daylight
- Cold exposure
- Novel challenges, deep work
- L-Tyrosine (if depleted)
Avoid overdriving:
- Too much switching, social scrolling
- Dopamine stacking (coffee + sugar + novelty)
Acetylcholine (Learning, Focused Attention)
Stimulate:
- Alpha-GPC / Citicoline (if needed)
- Eggs, fish, lion's mane
- Intervals of high-focus learning
Signs of low ACh:
- Mental fog
- Can't encode new info
Norepinephrine (Arousal, Wakefulness)
Stimulate:
- Breath holds (CO2 up)
- Caffeine + L-theanine
- HIIT or mobility drills
Watch for crash:
- Irritability, tunnel vision, heart rate too high
Serotonin (Mood, Patience, Stability)
Stimulate:
- Carbs + tryptophan foods
- Gratitude journaling
- Afternoon sunlight
Note: High dopamine <-> low serotonin (often inverse)
Detecting Neural Fatigue
| Symptom | Likely Cause |
|---|---|
| Mental fog, confusion | Adenosine buildup, ACh depletion |
| Irritability, high arousal | Norepinephrine burnout |
| Can't focus, task-switching | Dopamine depletion |
| Yawning, eye strain | Glucose drop, sleep pressure |
Interventions
| Method | Use When | Duration |
|---|---|---|
| 20-30 min nap | Early adenosine signs | < 30 min |
| Protein + low GI carbs | Glucose dip, fatigue | 15-45 min |
| Fast walk + breathwork | Arousal reset | 10 min |
| Nature / no-stim break | Total overload | 30-60 min |
Default Mode Network
- Part of our brain that gets activated when we aren't focusing on any particular task.
- This are really fast and helps us with other tasks which make us think outside our reality past, future and others but not present.
- Meditation doesn't simulate Default mode network. It helps with being in the moment.
- It doesn't only help in the moment but also has effects on baseline(when you aren't meditating).