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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. Formula
2. Phenomena Explained
3. Canonical Computation
4. Relation to Attention
5. PyTorch — Divisive Normalization
6. Relation to Deep Learning
7. Mechanistic Implementation
8. Value Normalization (Economic Decision)
9. Adaptive Advantages
10. Common Pitfalls
10.1 Vision Only
10.2 Single Mechanism
10.3 Norm Pool = All Neurons
10.4 Linear Operation
10.5 Unrelated to Attention
11. Related Concepts
References