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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. Attractor Types
2. Dynamical Systems View
3. Point Attractor — Working Memory
4. Ring Attractor — Head Direction
5. Continuous Attractor — Grid/Place
6. PyTorch — Ring Attractor
7. Balance / Constraint
8. Decision = Attractor Competition
9. Relation to RNN
10. Common Pitfalls
10.1 Attractor = Hopfield Only
10.2 Persistent Activity = Only WM Mechanism
10.3 Recurrent → Necessarily Unstable
10.4 Attractors Static
10.5 Always Explicit Attractors
11. Related Concepts
References