Design of spatially extended neural networks for specific applications

Abstract

The processes and mechanisms of biological neural development provide many powerful insights for the creation of artificial neural systems. Biological neural systems are, in general, much more effective in carrying out tasks such as face recognition and motion detection than artificial neural networks. An important difference between biological and (most) artificial neurons is that biological neurons have extensive treeshaped neurites (axons and dendrites) that are themselves capable of active signal transduction and integration. In this paper we present a model, inspired by the processes of neural development, which leads to the growth and formation of neuron-to-neuron connections. The neural architectures created have treeshaped neurites and contain spatial information on branch and synapse positions. Furthermore, we have prototyped a simple but efficient way of simulating signal transduction along neurites using a finite state automaton (FSA). We expect that the combination of our neuronal development method with the FSA that mimics signal transfer, will provide an efficient and effective tool for exploring the relationship between neural form and network function.