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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. Core Idea
2. Math (Rao & Ballard 1999)
3. Neural Implementation
4. vs VAE / Free Energy
5. AI Connections
6. Empirical Support
7. PyTorch — Hierarchical Predictive Coding
8. Pathology
9. History
10. Common Pitfalls
10.1 Not Sole Framework
10.2 Limited Empirical Evidence
10.3 vs Backprop
10.4 Abstract vs Concrete
10.5 Doesn't Explain Hard Problem
11. Related Concepts
References