Alice
Project
Home
Simulation Replay
Worldview
Version Log
Mind Research
中文
🧠 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 Hypothesis
2. Infomax Principle
3. Predicted Neural Properties
4. Sparse Coding (Olshausen & Field 1996)
5. Redundancy Reduction vs Exploitation
6. PyTorch — Sparse Coding (Olshausen)
7. Adaptation
8. Relation to AI
9. Normative Methodology
10. Common Pitfalls
10.1 Complete Redundancy Removal
10.2 Infomax Sole Objective
10.3 Explains All RFs
10.4 Sparser = Better
10.5 Normative = Real Mechanism
11. Related Concepts
References