Evaluating performance in three-dimensional fluorescence microscopy

In biological fluorescence microscopy, image contrast is often degraded by a high background arising from out of focus regions of the specimen. This background can be greatly reduced or eliminated by several modes of thick specimen microscopy, including techniques such as 3-D deconvolution and confocal. There has been a great deal of interest and some confusion about which of these methods is ‘better’, in principle or in practice. The motivation for the experiments reported here is to establish some rough guidelines for choosing the most appropriate method of microscopy for a given biological specimen. The approach is to compare the efficiency of photon collection, the image contrast and the signal-to-noise ratio achieved by the different methods at equivalent illumination, using a specimen in which the amount of out of focus background is adjustable over the range encountered with biological samples. We compared spot scanning confocal, spinning disk confocal and wide-field/deconvolution (WFD) microscopes and find that the ratio of out of focus background to in-focus signal can be used to predict which method of microscopy will provide the most useful image. We also find that the precision of measurements of net fluorescence yield is very much lower than expected for all modes of microscopy. Our analysis enabled a clear, quantitative delineation of the appropriate use of different imaging modes relative to the ratio of out-of-focus background to in-focus signal, and defines an upper limit to the useful range of the three most common modes of imaging

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation

Modeling and visualizing networked multi-core embedded software energy consumption

In this report we present a network-level multi-core energy model and a software development process workflow that allows software developers to estimate the energy consumption of multi-core embedded programs. This work focuses on a high performance, cache-less and timing predictable embedded processor architecture, XS1. Prior modelling work is improved to increase accuracy, then extended to be parametric with respect to voltage and frequency scaling (VFS) and then integrated into a larger scale model of a network of interconnected cores. The modelling is supported by enhancements to an open source instruction set simulator to provide the first network timing aware simulations of the target architecture. Simulation based modelling techniques are combined with methods of results presentation to demonstrate how such work can be integrated into a software developer's workflow, enabling the developer to make informed, energy aware coding decisions. A set of single-, multi-threaded and multi-core benchmarks are used to exercise and evaluate the models and provide use case examples for how results can be presented and interpreted. The models all yield accuracy within an average +/-5 % error margin