Optimal In-Place Learning and the Lobe Component Analysis

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Optimal In-Place Learning and the Lobe Component Analysis
— It is difficult to map many existing learning algorithms onto biological networks because the former require a separate learning network. The computational basis of biological cortical learning is still poorly understood. This paper rigorously introduces a concept called in-place learning. With in-place learning, every networked neuron in-place is responsible for the learning of its signal processing characteristics (e.g., efficacies of synapses) within its connected network environment. There is no need for a separate learning network. With this in-place hypothesis, consequently, each neuron does not have extra space to compute and store the second and higher order statistics (e.g., correlations) of its input fibers. This work first provides a classification of learning algorithms. Then, it shows that the two well-known in-place biological mechanisms, the Hebbian rule and lateral inhibition, are sufficient to develop orientation selective cells, similar to those found in V1,...
Juyang Weng, Nan Zhang 0002
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Authors Juyang Weng, Nan Zhang 0002
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