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.

No abstract

No abstract

No abstract

A model of neural processing is proposed which is able to incorporate a great deal of neurophysiological detail, including effects associated with the mechanism of postsynaptic summation, cell surface geometry and axo-axonal interactions and is capable of hardware realisation (PRAM). The model is an extension of earlier work by the authors, which by operating at much shorter time scales (of the order of the lifetime of a quantum of neurotransmitter in the synaptic cleft) allows a greater amount of information to be retrieved from the simulated spike train. The mathematical framework for the model appears to be that of an extended Markov process (involving the firing histories of the N neurons); simulations of single units have yielded results in excellent agreement with theoretical predictions. The extended neural model is expected to be particularly applicable in situations where timing constraints are of special importance (such as the auditory cortex) or where firing thresholds are high, as is the case for the granule and pyramidal cells of the hippocampus. as a'probabilistic random access memory' RECONSTRUCTION OF PHOTORECEPTOR RESPONSES IN NUDIBRANCH MOLLUSC, EERMISSENDA CRASSICORNIS, BASED ON ISOLATED IONIC CURRENTS Hidelashi IKENO, Manabo SAKAKIBARA, Shim USUI, and Daniel L ALKON' Dcprtmcnt of Infomotion and Computer S e t m m Toyohoaho Unroorsily of Technology Temptu-do, Toyohasht 440, JAPAN and 'Labomlory 01 Molecalor and Cellular Neurobiology AbstractThe nudibranch mollusc, Hermissenda Crassicornis, is known to be capable of associative learning. Much evidence has been accumulated on the neurophysiological, morphological and biochemical snbstrates of associative memory at the cellular level of the identified neuron, the type B photoreceptor. In order to gain an analytical understandig of the learning induced changes in the type B cell as well as a helpful insight into the cellular learning mechanisms, we have formulated a mathematical description of the type B cell in terms of the corresponding ionic currents. The model response has been compared to experimental data from a voltage clamp study with a command of step and triangular function. In addition to the cell model, a voltage clamp system has also been included in this simulation to allow us to compare the model cell responses to the actual ones with ease. Simulation has shown that decrease of the two potassium conductances, gAma+ and gCa.-Kma+, caused the light response to be enhanced and the membrane input impedance to be increased. Although the input impedance has been believed to increase constantly following the acquisition of learning, our result suggests that it increases transiently soon after the cessation of light stimulus.The human memory (Instantaneous, Short-Tern, Long, Term) is functionally distributed in the brain cortex and subcortex, in the periphery nerve system. The storage of information in human memory is distributed as every section of the retain (remember) neuron-glia environment contains elements of all mnemoengrams (traces)...