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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. Bayes' Rule
2. Brain Applications
2.1 Perception
2.2 Motor
2.3 Learning
3. Classic Experimental Evidence
3.1 Cue Combination
3.2 Optical Illusions
3.3 Motor Adaptation
4. Free Energy Framework (Friston)
5. Active Inference
6. vs AI
7. PyTorch — Bayesian Inference Toy
8. Neural Implementation
9. Pathology
10. Limits
11. Common Pitfalls
11.1 Bayesian ≠ Optimal
11.2 Prior Hard to Quantify
11.3 Computation Feasibility
11.4 With Free Will
11.5 Cultural Variation
12. Related Concepts
References