Can Neuronal Networks be Used in Data-Poor Situations?

Abstract

Perhaps the greatest problem that is faced in most attempts to use artificial neuronal networks for ecological applications is that the quantity of data is often very limited. Although there are a few cases where large amounts of data are available, as in the case of remote sensing or observations based on automatic telemetry, it is far more common to have to deal with limited and irregularly spaced data, and the data may not always be strictly comparable due to variations in environmental conditions between sampling periods. In most situations the collection of field data is both time-consuming and expensive. Since the training and testing of neuronal networks is very data-intensive, this poses serious obstacles to the development of neuronal network applications in ecology.