Neocortical layer 4 as a pluripotent function linearizer.
A highly effective kernel-based strategy used in machine learning is to transform the input space into a new "feature" space where nonlinear problems become linear and more readily solvable with efficient linear techniques. We propose that a similar "problem-linearization" strategy is used by the neocortical input layer 4 to reduce the difficulty of learning nonlinear relations between the afferent inputs to a cortical column and its to-be-learned upper layer outputs. The key to this strategy is the presence of broadly tuned feed-forward inhibition in layer 4: it turns local layer 4 domains into functional analogs of radial basis function networks, which are known for their universal function approximation capabilities. With the use of a computational model of layer 4 with feed-forward inhibition and Hebbian afferent connections, self-organized on natural images to closely match structural and functional properties of layer 4 of the cat primary visual cortex, we show that such layer-4-like networks have a strong intrinsic tendency to perform input transforms that automatically linearize a broad repertoire of potential nonlinear functions over the afferent inputs. This capacity for pluripotent function linearization, which is highly robust to variations in network parameters, suggests that layer 4 might contribute importantly to sensory information processing as a pluripotent function linearizer, performing such a transform of afferent inputs to a cortical column that makes it possible for neurons in the upper layers of the column to learn and perform their complex functions using primarily linear operations.
Department of Biomedical Engineering, University of North Carolina School of Medicine, Chapel Hill, NC 27599-7545, USA. favorov@bme.unc.edu
Oleg V
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Favorov
Olcay
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D000818:Animals; D002415:Cats; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D019579:Neocortex / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*