How the (Tensor-) Brain uses Embeddings and Embodiment to Encode Senses and Decode Symbols
<|reference_start|>How the (Tensor-) Brain uses Embeddings and Embodiment to Encode Senses and Decode Symbols: The tensor brain has been introduced as a computational model for perception and memory. We provide an overview of the tensor brain model, including recent developments. The tensor brain has two major layers: the representation layer and the index layer. The representation layer is a model for the subsymbolic global workspace from consciousness research. The state of the representation layer is the cognitive brain state. The index layer contains symbols for concepts, time instances, and predicates. In a bottom-up operation, the cognitive brain state is encoded by the index layer as symbolic labels. In a top-down operation, symbols are decoded and written to the representation layer. This feeds to earlier processing layers as embodiment. The top-down operation became the basis for semantic memory. The embedding vector of a concept forms the connection weights between its index and the representation layer. The embedding is the signature or ``DNA'' of a concept, which is decoded by the brain when its index is activated. It integrates all that is known about a concept from different experiences, modalities, and symbolic decodings. Although being computational, it has been suggested that the tensor brain might be related to the actual operation of the brain. The sequential nature of symbol generation might have been a prerequisite to the generation of natural language. We describe an attention mechanism and discuss multitasking by multiplexing. We emphasize the inherent multimodality of the tensor brain. Finally, we discuss embedded and symbolic reasoning.<|reference_end|>
@article{tresp2024how,
    title={How the (Tensor-) Brain uses Embeddings and Embodiment to Encode Senses
  and Decode Symbols},
    author={Volker Tresp and Hang Li},
    journal={arXiv preprint arXiv:2409.12846},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12846},
    primaryClass={cs.AI cs.LG cs.NE q-bio.NC}
}