  We introduce bio-inspired artificial neural networks consisting of neurons
that are additionally characterized by spatial positions. To simulate
properties of biological systems we add the costs penalizing long connections
and the proximity of neurons in a two-dimensional space. Our experiments show
that in the case where the network performs two different tasks, the neurons
naturally split into clusters, where each cluster is responsible for processing
a different task. This behavior not only corresponds to the biological systems,
but also allows for further insight into interpretability or continual
learning.
