Neocortical plasticity: an unsupervised cake but no free lunch

TL;DR

This paper explores how unsupervised local learning rules inspired by neocortical physiology could improve data efficiency and robustness in artificial neural networks, offering a promising alternative to back-propagation.

Contribution

It proposes new unsupervised learning approaches based on neocortical physiology that could enhance deep learning models' efficiency and generalization capabilities.

Findings

Potential for improved data efficiency in deep networks

Enhanced domain adaptation and generalization

Reduced susceptibility to adversarial attacks

Abstract

The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, important inspiration for the development of artificial intelligence systems has come from the study of natural systems of intelligence, the mammalian neocortex in particular. On the other, important inspiration for models and theories of the brain have emerged from artificial intelligence research. A central question at the intersection of these two areas is concerned with the processes by which neocortex learns, and the extent to which they are analogous to the back-propagation training algorithm of deep networks. Matching the data efficiency, transfer and generalization properties of neocortical learning remains an area of active research in the field of deep learning. Recent advances in our understanding of neuronal, synaptic and dendritic physiology of the neocortex suggest new approaches for unsupervised representation learning, perhaps through a new class of objective functions, which could act alongside or in lieu of back-propagation. Such local learning rules have implicit rather than explicit objectives with respect to the training data, facilitating domain adaptation and generalization. Incorporating them into deep networks for representation learning could better leverage unlabelled datasets to offer significant improvements in data efficiency of downstream supervised readout learning, and reduce susceptibility to adversarial perturbations, at the cost of a more restricted domain of applicability.