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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. SNN vs ANN
2. Neuron Models
2.1 LIF (Leaky Integrate-and-Fire)
2.2 Izhikevich
2.3 Hodgkin-Huxley
3. STDP Learning
4. Surrogate Gradient (Training SNN)
5. PyTorch — LIF SNN
6. Neuromorphic Hardware
7. Applications (SNN Advantages)
8. Limitations
9. Common Pitfalls
9.1 SNN Not Necessarily More Efficient
9.2 Spike Count ≠ Firing Rate
9.3 STDP Limits
9.4 LIF Lacks Biological Detail
9.5 Brain ≠ SNN
10. Related Concepts
References