Scenario 3: Brain-Inspired Learning¶
Tutorials on biologically-inspired learning rules for neural networks, including Hebbian plasticity and associative memory.
Tutorials¶
Learning Rules
Overview¶
This scenario explores brain-inspired learning algorithms that model synaptic plasticity mechanisms observed in biological neural systems.
Tutorial 1: Pattern Storage and Recall
Hebbian learning principle
Hopfield networks for associative memory
One-shot learning and pattern completion
Anti-Hebbian learning for decorrelation
Learning Objectives¶
By completing this tutorial, you will:
Understand biological learning rules and their computational properties
Implement Hebbian and anti-Hebbian learning algorithms
Train Hopfield networks for associative memory
Apply brain-inspired learning to pattern recognition tasks
Prerequisites¶
Completed Scenario 1 (CANN Modeling) or equivalent knowledge
Understanding of neural network basics
Familiarity with unsupervised learning concepts
Estimated Time¶
Tutorial 1: 30-35 minutes
When to Use These Methods¶
Hebbian/Hopfield: - Associative memory tasks - Pattern completion with partial inputs - One-shot learning scenarios
Anti-Hebbian: - Lateral inhibition between neurons - Decorrelation and sparse coding - Winner-take-all competition
Future Additions¶
Additional tutorials on advanced learning rules (BCM plasticity, Oja’s rule, STDP) may be added in future releases.