Probabilistic skeletons endow brain-like neural networks with innate computing capabilities

C. Stoeckl, D. Lang, and W. Maass

Abstract:

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 remained unknown how nature achieves this. Insight from artificial neural networks does not help to solve this problem, since their computational capabilities 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 capabilities of artificial neural networks and neuromorphic hardware through clever initialization.

Reference: C. Stoeckl, D. Lang, and W. Maass. Probabilistic skeletons endow brain-like neural networks with innate computing capabilities. BioRxiv, 2021.