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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. Paradigm Shift
2. State Space
3. Low-Dimensional Manifold
4. Churchland 2012 — Rotational Dynamics
5. Dynamical Systems View
6. PyTorch — jPCA Idea (Rotational Component)
7. dPCA — Demixed PCA
8. Mixed Selectivity
9. RNN as Model
10. Common Pitfalls
10.1 Single-Neuron Tuning Sufficient
10.2 Low-Dim = Simple
10.3 PCA Components Have Biological Meaning
10.4 Manifold Fixed
10.5 Representation vs Dynamics Either-Or
11. Related Concepts
References