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🧠 Neuroscience
Foundations
Neuroanatomy
Cellular & Molecular Neuroscience
Systems Neuroscience
Cognitive Neuroscience
Computational Neuroscience
Brain-Computer Interface
Neurotech Frontiers
Neuro Disorders
Computational Neuroscience
Spiking Neural Network (SNN) — Neuromorphic Computing
Predictive Coding
Bayesian Brain
Hopfield Networks
Grid Cells — Neural Spatial Map
Reinforcement Learning in the Brain
Free Energy Principle
Attractor Networks
Neural Population Dynamics
Drift-Diffusion Model
Efficient Coding Hypothesis
Divisive Normalization
Synaptic Plasticity Models
Deep Learning vs Brain
Reservoir Computing
Compartmental Models
Whole-Brain Modeling
Information Theory in Neuroscience
Neural ODEs & Continuous-Time Models
Energy-Based Models & the Brain
Table of Contents
1. Why Whole-Brain
2. Modeling Levels
3. Neural Mass Model
4. The Virtual Brain (TVB)
5. PyTorch — Simplified Whole-Brain Network
6. Structure → Function
7. Clinical Applications
8. Blue Brain / HBP Debate
9. Relation to AI
10. Common Pitfalls
10.1 More Detailed Better
10.2 SC = FC
10.3 Fitting FC = Understanding
10.4 Neural Mass = Real Population
10.5 Virtual Brain = Consciousness / AGI
11. Related Concepts
References