  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.
