Genetically encoded structure endows neural networks of the brain with innate computational capabilities that enable odor classification and basic motor control right after birth. It is also conjectured that the stereotypical laminar organization of neocortical microcircuits provides basic computing capabilities on which subsequent learning can build. However, it has re-mained unknown how nature achieves this. Insight from artificial neural networks does not help to solve this problem, since their computational ca-pabilities result from learning. We show that genetically encoded control over connection probabilities between different types of neurons suffices for programming substantial computing capabilities into neural networks. This insight also provides a method for enhancing computing and learning ca-pabilities of artificial neural networks and neuromorphic hardware through clever initialization.
|Publication status||Published - 2021|
|Name||bioRxiv - the Preprint Server for Biology|
|Publisher||Cold Spring Harbor Laboratory Press|