Towards a Cross-Level Theory of Neural Learning.

This paper reviews ideas and results from unsupervised learning theory that have given the best explanation yet of how neural firing rates self-organise to code natural images in area V1 of visual cortex. It then discusses the generalisation of these ideas to self-organising spike-coding networks. A mismatch between the resulting spike-learning algorithm and the known physiological processes of synaptic plasticity is then used as a motivation to introduce the rather obvious idea that neurons are not sending their information to other neurons, but to synapses—more microscopic structures. This prompts a survey of other inter-level communications in the brain and inside cells. It is proposed on the basis of this that information flows all the way up and down the reductionist hierarchy—an idea that transforms many of our ideas about machine learning and neuroscience. What it transforms them into is not yet clear, but the remainder of the paper discusses this. [ABSTRACT FROM AUTHOR]