  We introduce organism networks, which function like a single neural network
but are composed of several neural particle networks; while each particle
network fulfils the role of a single weight application within the organism
network, it is also trained to self-replicate its own weights. As organism
networks feature vastly more parameters than simpler architectures, we perform
our initial experiments on an arithmetic task as well as on simplified
MNIST-dataset classification as a collective. We observe that individual
particle networks tend to specialise in either of the tasks and that the ones
fully specialised in the secondary task may be dropped from the network without
hindering the computational accuracy of the primary task. This leads to the
discovery of a novel pruning-strategy for sparse neural networks
