Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing

arXiv:2207.07338 · cs.NE, cs.AI, cs.LG · Submitted 2022-07-15 · Read on arXiv

cs.NE, cs.AI, cs.LG

Submitted: 2022-07-15

Updated: 2026-09-24

Terminology

• Activation Function — 4× in this paper · explained in 1 episode
• Loss Function — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Cooperation — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Cognitive Load — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Variational Autoencoder (VAE) — 2× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Affective Computing — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Energy Efficiency — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes

Sources

• How Can We Be So Dense? The Benefits of Using Highly Sparse Representations
• The Power of Sparsity in Convolutional Neural Networks

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