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🧠 Neuroscience
Foundations
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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 Idea
2. Lineage
3. Boltzmann Machine
4. Biological Correspondence
5. PyTorch — RBM (Contrastive Divergence)
6. 2024 Nobel Physics
7. Energy ↔ Inference
8. Relation to Diffusion / Modern Generative
9. Difficulty — Partition Function
10. Common Pitfalls
10.1 EBM = Hopfield
10.2 Energy Has Physical Meaning
10.3 Brain Computes Partition Function
10.4 Boltzmann Machine Practical
10.5 Energy Minima = Only Computation
11. Related Concepts
References