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🧠 Neuroscience
Foundations
Neuroanatomy
Cellular & Molecular Neuroscience
Systems Neuroscience
Cognitive Neuroscience
Computational Neuroscience
Brain-Computer Interface
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. Core Idea
2. Two Schools
3. Echo State Property
4. PyTorch — Echo State Network
5. Edge of Chaos
6. Biological Correspondence
7. FORCE Learning (Sussillo & Abbott 2009)
8. Pros and Cons
9. Physical / Neuromorphic Reservoir
10. Common Pitfalls
10.1 Reservoir Needs Training
10.2 Bigger Always Better
10.3 = Ordinary RNN
10.4 Edge of Chaos Mysteriously Optimal
10.5 Biology Is Just a Reservoir
11. Related Concepts
References