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Advanced Memristive Learning for Neuromorphic Computing
This module implements sophisticated memristive computing paradigms including crossbar arrays with multiple device types, advanced plasticity mechanisms, homeostatic regulation, metaplasticity, and neuromodulation for spatial learning.
Structs§
- Advanced
Memristive Learning - Advanced memristive learning system with synaptic plasticity and homeostasis
- Consolidation
Event - Memory consolidation event
- Consolidation
Rules - Memory consolidation rules
- Forgetting
Protection Rules - Forgetting protection mechanisms
- Homeostatic
System - Homeostatic regulation system
- Learning
History - Learning history tracking
- Learning
Rate Adaptation - Learning rate adaptation mechanisms
- Memristive
Crossbar - Memristive crossbar array with advanced properties
- Metaplasticity
Rules - Metaplasticity rules for learning-to-learn
- Neuromodulation
Effects - Effects of neuromodulation on plasticity
- Neuromodulation
System - Neuromodulation system for context-dependent learning
- Neuromodulator
Release Patterns - Neuromodulator release patterns
- Performance
Metrics - Performance metrics for learning assessment
- Plasticity
Event - Plasticity event recording
- Plasticity
Learning Rates - Learning rates for different plasticity components
- Plasticity
Mechanism - Synaptic plasticity mechanisms
- Plasticity
Thresholds - Threshold parameters for plasticity
- Plasticity
Time Constants - Time constants for plasticity mechanisms
- Threshold
Adaptation - Threshold adaptation for dynamic learning
- Training
Result - Training result structure
Enums§
- Consolidation
Type - Types of memory consolidation
- Homeostatic
Mechanism - Types of homeostatic mechanisms
- Memristive
Device Type - Types of memristive devices
- Plasticity
Event Type - Types of plasticity events
- Plasticity
Type - Types of synaptic plasticity