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🧠 Neuroscience
Foundations
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Cellular & Molecular Neuroscience
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Cognitive Neuroscience
Computational Neuroscience
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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. RL Framework
2. Dopamine = RPE (Schultz 1997)
3. TD Learning
4. Basal Ganglia = Actor-Critic
5. Model-free vs Model-based
6. PyTorch — TD Learning (DA model)
7. Beyond Classic RPE
8. Serotonin + Others
9. AI ↔ Brain Bidirectional
10. Common Pitfalls
10.1 DA = Pleasure
10.2 DA Only Encodes Mean Reward
10.3 Brain Only Model-free
10.4 RPE Explains All Learning
10.5 One δ for Whole Brain
11. Related Concepts
References