  This research report introduces ElegansNet, a neural network that mimics
real-world neuronal network circuitry, with the goal of better understanding
the interplay between connectome topology and deep learning systems. The
proposed approach utilizes the powerful representational capabilities of living
beings' neuronal circuitry to design and generate improved deep learning
systems with a topology similar to natural networks. The Caenorhabditis elegans
connectome is used as a reference due to its completeness, reasonable size, and
functional neuron classes annotations. It is demonstrated that the connectome
of simple organisms exhibits specific functional relationships between neurons,
and once transformed into learnable tensor networks and integrated into modern
architectures, it offers bio-plausible structures that efficiently solve
complex tasks. The performance of the models is demonstrated against randomly
wired networks and compared to artificial networks ranked on global benchmarks.
In the first case, ElegansNet outperforms randomly wired networks.
Interestingly, ElegansNet models show slightly similar performance with only
those based on the Watts-Strogatz small-world property. When compared to
state-of-the-art artificial neural networks, such as transformers or
attention-based autoencoders, ElegansNet outperforms well-known deep learning
and traditional models in both supervised image classification tasks and
unsupervised hand-written digits reconstruction, achieving top-1 accuracy of
99.99% on Cifar10 and 99.84% on MNIST Unsup on the validation sets.
