No abstract

Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.

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Abstract-There are several institutions that accredit educational programs and require documentation to ensure that an educational program regularly meets certain criteria. This paper focuses on ABET program. They require that programs show student achievement and certain course outcomes. Documentation of this requirement is particularly burdensome. There is no standard method of generating these reports, so each institution handles it differently. This might involve manual collection of the data which is time very consuming. A software tool that facilitates this collection of the data and automatically generates the required reports would save institutions time and money. This paper presents such a tool, named ACAT (ABET Course Assessment Tool), it is a web-based application designed to assist in the collecting of data and generation of standardized assessment reports. This paper focuses on design and usability aspects of the proposed ACAT tool and provides implementation and operation details.

Abstract-We attack the problem of user fatigue in using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines computable user interface design metrics with subjective user input to guide evolution. Individuals in our population represent interface specifications and we compute an individual's fitness from a weighted combination of user input and user-interface-design guidelines. Results from our preliminary study involving three users indicates that users are able to effectively bias evolution towards user interface designs that reflect both user preferences and computed guideline metrics. Furthermore, we can reduce fatigue, defined by the number of choices needing to be made by the human designer, by doing two things. First, asking the user to pick just two (the best and worst) user interfaces from among a subset of nine shown. Second, asking the user to make the choice once every t generations, instead of every single generation. Our goal is to provide interface designers with an interactive tool that can be used to explore innovation and creativity in the design space of user interfaces and make it easier for end-users to further customize their interface without programming knowledge.