Flow cytometry has been used over the past 5 years to begin detailed exploration of the distribution and abundance of picoplankton in the oceans. Light scattering and fluorescence measurements on individual plankton cells in seawater samples allow construction of population signatures from size and pigment characteristics. The use of "list mode" data has made these studies possible, but on-shore analysis of copious data does not permit on-site reexamination of important or unexpected observations, and overall effort is greatly handicapped by data analysis time.Here we describe the application of neural net computer technology to the analysis of flow cytometry data. Although the data used in this study are from oceanographic research, the results are general and should be directly applicable to flow cytometry data of any sort.Neural net computers are ideally suited to perform the pattern recognition required for the quantitative analysis of flow cytometry data. Rather than being programmed to perform analysis, the neural net computer is "taught" how to analyze the cell populations by presenting examples of inputs and correct results. Once the system is "trained," similar data sets can be analyzed rapidly and objectively, minimizing the need for laborious user interaction. The neural network described here offers the advantages of 1) adaptability to changing conditions and 2) potential realtime analysis. High accuracy and processing speed near that required for real-time classification have been achieved in a software simulation of the neural network on a Macintosh@ SE personal computer. In the past 5 years, the technology of flow cytometry has been assimilated into oceanography from biomedical research, and it shows great promise for contributions in phytoplankton ecology (3,211. These instruments have begun to serve as a new form of microscope in oceanographic research, one that identifies organisms not through pattern recognition of the human eye, but through multiparameter characterization and discell types over vast regions of the oceans (3,4,12,14), to characterize changes in size and fluorescence properties of cells with depth (lo), to distinguish between cell types that cannot be distinguished by any other methods (12,20), and to detect populations of cells too small to be studied microscopically (4). Flow cytometry has also been used to study plankton physiology and ecology in the laboratory. Cell cycle progression and differcrimination according to predesignated objective criteria. This technology has revolutionized the way phytoplankton and particle dynamics at sea are studied and, in turn, has changed our concepts of the processes we are trving to model and understand.

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