Towards a Cross-Level Theory of Neural Learning

This talk 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 that 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 talk discusses of this.

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