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The Tensor Brain: A Unified Theory of Perception, Memory, and Semantic Decoding
Volker Tresp1, Sahand Sharifzadeh2, Hang Li3
1LMU Munich and Siemens Munich, Germany volker.tresp@lmu.de.
Neural Computation
|November 23, 2022
Summary
This study introduces a unified computational theory of perception and memory using a bilayer tensor network (BTN). The model explains how symbolic and subsymbolic layers interact to form concepts, enabling agents to learn and make decisions for future actions.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Artificial Intelligence
Background:
- Current models of perception and memory lack a unified computational framework.
- Understanding the interplay between symbolic and subsymbolic processing is crucial for artificial general intelligence.
Purpose of the Study:
- To present a unified computational theory of an agent's perception and memory.
- To model perception and memory using oscillating interactions within a bilayer tensor network (BTN).
- To demonstrate how this model can support present and future decision-making.
Main Methods:
- Developed a bilayer tensor network (BTN) with symbolic index and subsymbolic representation layers.
- Modeled perception and memory as operational modes of the BTN.
- Implemented grounding of indices via subsymbolic embeddings and connection weights.
- Tested the model on an expanded benchmark dataset.
Main Results:
- Perception and memory are unified through the BTN's interaction modes.
- The model grounds symbolic indices in subsymbolic representations.
- Episodic and semantic memory support present actions and future planning.
- Self-supervised learning within the model acquires and refines concepts.
Conclusions:
- The BTN model provides a unified computational theory for perception and memory.
- This framework is essential for understanding agent decision-making and future planning.
- A deeper understanding of perception and memory is a prerequisite for human-level intelligence.
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