Optimal In-Place Self-Organization for Cortical Development: Limited Cells, Sparse Coding and Cortical Topography

Cortical self-organization during open-ended development is a core issue for perceptual development. Traditionally, unsupervised learning and supervised learning are two different types of learning conducted by different networks. However, there is no evidence that the biological nervous system treats them in a disintegrated way. The computational model presented here integrates both types of learning using a new biologically inspired network whose learning is in-place. By in-place learning, we mean that each neuron in the network learns on its own while interacting with other neurons. There is no need for a separate learning network. We present in this paper the Multi-layer In-place Learning Network (MILN) for regression and classification. This work concentrates on its two-layer version for global pattern detection (without incorporating an attention selection mechanism). It reports properties about limited cells, sparse coding and cortical topography. The network enables both unsupervised and supervised learning to occur concurrently. Within each layer, the adaptation of each neuron is nearly-optimal in the sense of the least possible estimation error given the observations. Experimental results are presented to show the effects of the properties investigated.