Learning Based on CC1 and CC4 Neural Networks

2017/12/22 by Subhash Kak, Kak, Subhash
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #cs.NE

paper · pdf · doi:10.48550/arxiv.1712.09331

5 pages.arXiv admin note: text overlap with arXiv:0809.5087; text overlap with arXiv:cs/0601129 by other authors

arxiv created 2017/12/22 · openalex publication_date 2017/12/22 · arxiv updated 2017/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose that a general learning system should have three kinds of agents corresponding to sensory, short-term, and long-term memory that implicitly will facilitate context-free and context-sensitive aspects of learning. These three agents perform mututally complementary functions that capture aspects of the human cognition system. We investigate the use of CC1 and CC4 networks for use as models of short-term and sensory memory.

Citations

• Intriguing properties of neural networks
• Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity
• Deep Learning in Neural Networks: An Overview
• Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention
• Deep Neural Networks are Easily Fooled: High Confidence Predictions for\n Unrecognizable Images
• Hybrid Neural Network Architecture for On-Line Learning
• Instantaneously Trained Neural Networks

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