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,...

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Juyang Weng, Nan Zhang 0002
• Keywords: IJCNN 2006, In-place, In-place Learning, Learning Algorithm