  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.
