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🧠 Neuroscience
Foundations
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Neurotech Frontiers
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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 Claim
2. Math
3. Three Minimization Routes
4. Active Inference
5. Relation to Predictive Coding
6. PyTorch — Variational Free Energy Minimization
7. Relation to VAE / ELBO
8. Markov Blanket
9. Criticisms
10. Common Pitfalls
10.1 FEP = Predictive Coding
10.2 Free Energy = Thermodynamic Free Energy
10.3 Already Confirmed
10.4 Replaces RL
10.5 Minimize Surprise = Seek Comfort (Dark Room Problem)
11. Related Concepts
References