Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization

Keyphrases

• Recurrent Neural Network 100%
• Attractor Manifolds 100%
• Gradient-based Optimization 100%
• Coexisting Attractors 100%
• Simultaneous Embedding 100%
• Steady State 66%
• Activity Patterns 66%
• Loss Function 66%
• Energy Landscape 66%
• Postsynaptic Density Protein 95 (PSD-95) 66%
• Population Activity 66%
• Synaptic Connectivity 66%
• Working Memory 33%
• Local Minima 33%
• Discrete Set 33%
• Interference Effect 33%
• State Form 33%
• Weight Adjustment 33%
• Continuous Attractors 33%
• Hippocampal Area 33%
• Systematic Drift 33%
• Area CA3 33%
• Quenched Noise 33%

Computer Science

• Recurrent Neural Network 100%
• Synaptic Weight 66%
• Local Minimum 33%
• Continuous Variable 33%

Neuroscience

• Recurrent Neural Network 100%
• Working Memory 33%