A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons

Keyphrases

• Learning Framework 100%
• Sparsity 100%
• Growth Transforms 100%
• Backpropagation 100%
• Spiking Activity 75%
• Learning Approaches 50%
• Network Optimize 50%
• Dynamic Range 25%
• Classification Accuracy 25%
• Publicly Available 25%
• Spike Response 25%
• Network Level 25%
• Network Architecture 25%
• Structural Constraints 25%
• Network Structure 25%
• Lagrangian 25%
• Population Model 25%
• Energy Functional 25%
• Supervised Learning 25%
• Unsupervised Learning 25%
• Energy Structure 25%
• Constraint Violation 25%
• Proposed Formulation 25%
• Distributed Energy 25%
• Spiking Network 25%
• Energy Resources 25%
• Sensor Drift 25%
• Training Error 25%
• Connectivity Constraint 25%
• TinyML 25%
• Sparsity Constraint 25%
• Spike Statistics 25%
• Resource-efficient Learning 25%
• Resource Structure 25%
• Transient Population Dynamics 25%
• Local Learning Rules 25%
• Neuromorphic 25%
• Transform Network 25%
• Machine Olfaction 25%
• Energy Networks 25%

Computer Science

• Learning Framework 100%
• Sparsity 100%
• Learning Approach 50%
• Network Architecture 25%
• Case Study 25%
• Classification Accuracy 25%
• Supervised Learning 25%
• Unsupervised Learning 25%
• Trained Network 25%
• Energy Functional 25%
• Constraint Violation 25%
• Training Error 25%
• Network Structure 25%