A learning algorithm for computational connected cellular network

The objective of computational connected cellular network (CCCN) is to model a network of bone cells and study the mechanical loading induced signal communication pattern among them. Our previous study (2000, 2001) has shown that a backpropagation (BP) neural network model can be used to capture the functional relation between the mechanical loading and the amount of bone formation. To emulate the cell-to-cell communication pattern in bone matrix, a new computational connected cellular network (CCCN) learning system has been developed with a structure that closely mimics the actual biological structure of cell-connections in a bone. An error-correcting learning algorithm is proposed for CCCN based on a two-dimensional extension of the backpropagation algorithm. The CCCN is divided into numerous BP networks, whose architecture changes with weights and cell-state updating cycles. The conventional BP learning algorithm can be applied to each BP network. It is convergent because of the constraints enforced by the characteristics of a real bone cell. Application of the CCCN to an animal bone adaptation experiment produces interesting cell communication patterns.