Implementing engrams from a machine learning perspective: the relevance of a latent space
<|reference_start|>Implementing engrams from a machine learning perspective: the relevance of a latent space: In our previous work, we proposed that engrams in the brain could be biologically implemented as autoencoders over recurrent neural networks. These autoencoders would comprise basic excitatory/inhibitory motifs, with credit assignment deriving from a simple homeostatic criterion. This brief note examines the relevance of the latent space in these autoencoders. We consider the relationship between the dimensionality of these autoencoders and the complexity of the information being encoded. We discuss how observed differences between species in their connectome could be linked to their cognitive capacities. Finally, we link this analysis with a basic but often overlooked fact: human cognition is likely limited by our own brain structure. However, this limitation does not apply to machine learning systems, and we should be aware of the need to learn how to exploit this augmented vision of the nature.<|reference_end|>
@article{de lucas2024implementing,
    title={Implementing engrams from a machine learning perspective: the relevance
  of a latent space},
    author={J Marco de Lucas},
    journal={arXiv preprint arXiv:2407.16616},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16616},
    primaryClass={cs.NE cs.AI}
}