Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback
This work addresses the elusive principles of cortical computation for neuroscience and AI, proposing a model that integrates compression, prediction, and feedback, though it is incremental in building on existing anatomical knowledge.
The authors tackled the problem of understanding fundamental cortical computation principles by developing a phenomenological model of the primate visual cortex, which reproduces key aspects like Simple and Complex cells in V1 and achieves state-of-the-art visual tracking performance on novel objects.
There has been great progress in understanding of anatomical and functional microcircuitry of the primate cortex. However, the fundamental principles of cortical computation - the principles that allow the visual cortex to bind retinal spikes into representations of objects, scenes and scenarios - have so far remained elusive. In an attempt to come closer to understanding the fundamental principles of cortical computation, here we present a functional, phenomenological model of the primate visual cortex. The core part of the model describes four hierarchical cortical areas with feedforward, lateral, and recurrent connections. The three main principles implemented in the model are information compression, unsupervised learning by prediction, and use of lateral and top-down context. We show that the model reproduces key aspects of the primate ventral stream of visual processing including Simple and Complex cells in V1, increasingly complicated feature encoding, and increased separability of object representations in higher cortical areas. The model learns representations of the visual environment that allow for accurate classification and state-of-the-art visual tracking performance on novel objects.