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🧠 Neuroscience
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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. Hebbian and Its Problem
2. Oja's Rule (Normalization)
3. BCM Rule (Sliding Threshold)
4. STDP (Spike-Timing-Dependent)
5. Three-Factor Rule
6. PyTorch — BCM Rule
7. Homeostatic Plasticity
8. Bio-Plausible Learning
9. Relation to Backprop
10. Common Pitfalls
10.1 Hebbian Suffices
10.2 STDP Universal
10.3 One Rule Explains All
10.4 Plasticity = LTP/LTD Only
10.5 Three-factor = Backprop
11. Related Concepts
References