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The Tensor Brain: A Unified Theory of Perception, Memory, and Semantic Decoding.

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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.

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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.