Implementing engrams from a machine learning perspective: XOR as a basic motif
<|reference_start|>Implementing engrams from a machine learning perspective: XOR as a basic motif: We have previously presented the idea of how complex multimodal information could be represented in our brains in a compressed form, following mechanisms similar to those employed in machine learning tools, like autoencoders. In this short comment note we reflect, mainly with a didactical purpose, upon the basic question for a biological implementation: what could be the mechanism working as a loss function, and how it could be connected to a neuronal network providing the required feedback to build a simple training configuration. We present our initial ideas based on a basic motif that implements an XOR switch, using few excitatory and inhibitory neurons. Such motif is guided by a principle of homeostasis, and it implements a loss function that could provide feedback to other neuronal structures, establishing a control system. We analyse the presence of this XOR motif in the connectome of C.Elegans, and indicate the relationship with the well-known lateral inhibition motif. We then explore how to build a basic biological neuronal structure with learning capacity integrating this XOR motif. Guided by the computational analogy, we show an initial example that indicates the feasibility of this approach, applied to learning binary sequences, like it is the case for simple melodies. In summary, we provide didactical examples exploring the parallelism between biological and computational learning mechanisms, identifying basic motifs and training procedures, and how an engram encoding a melody could be built using a simple recurrent network involving both excitatory and inhibitory neurons.<|reference_end|>
@article{de lucas2024implementing,
    title={Implementing engrams from a machine learning perspective: XOR as a basic
  motif},
    author={Jesus Marco de Lucas, Maria Pe~na Fernandez, Lara Lloret Iglesias},
    journal={arXiv preprint arXiv:2406.09940},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09940},
    primaryClass={q-bio.NC cs.AI cs.NE}
}