Summary of Predictive Attractor Models, by Ramy Mounir and Sudeep Sarkar
Predictive Attractor Models
by Ramy Mounir, Sudeep Sarkar
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Sequential memory, crucial for biological and artificial intelligence, enables tasks like language comprehension, planning, and episodic memory formation. Existing methods suffer from limitations such as catastrophic forgetting, limited capacity, slow learning, and inability to represent multiple future possibilities. Inspired by neuroscience theories, the Predictive Attractor Models (PAM) architecture is proposed, featuring a streaming model that learns sequences online and avoids catastrophic forgetting through lateral inhibition in cortical minicolumns. PAM generates future predictions by sampling from predicted possibilities, realized through an attractor model trained alongside the predictor. The model is trained using Hebbian plasticity rules with local computations, offering desirable traits like noise tolerance, CPU-based learning, and capacity scaling. This breakthrough represents a significant step forward in sequential memory models, with implications for cognitive science and artificial intelligence research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to remember a sequence of events or actions, like a story you read yesterday. This is called sequential memory, and it’s important for our brains and computers. Current methods have some big problems, like forgetting what we learned before, not being able to learn new things quickly, and not being able to predict what might happen next. A new model called Predictive Attractor Models (PAM) is being developed, which can learn sequences online and remember them without forgetting what came before. It can also make predictions about what might happen in the future. This is a big step forward for understanding how our brains work and making better computers. |
