10774169
Significance of random neuronal drive
Abstract Effectively random firing occurs frequently in the brain. Random components of neuronal drive are therefore often major influences on activity. We describe here how level of drive, inhibitory/excitatory balance, correlation between input streams, EPSP/IPSP characteristics, the nature of the postsynaptic neuron all affect pattern and level of firing, the influence often interacting in complex ways. Random firing might also be instrumental in facilitating synchronized firing. These factors are possibly important elements of the mechanisms by which some neural networks encode information. Clarifying them might therefore facilitate a deeper understanding of the nature of the neural code.
11160456
Structure of Lateral Inhibition in an Olfactory Bulb Model
It has been shown that mutual lateral inhibition of the projection neurones in the olfactory bulb, mediated by interneurones, serves to tune the representation of odours in the bulb and reduce the overlap between similaz odorants. In this paper we demonstrate that the parameters of the lateral interaction, specifically the relation of synaptic strength to cell separation and the effective overall gain of the network, have a significant effect on the strength and range of lateral inhibition in a simple model of the olfactory bulb.
206848726
Viewing a Class of Neurodynamics on Parameter Space
Nearly all models in neural networks start from the assumption that the input-output characteristic is a sigmoidal function. On parameter space we present a systematic and feasible method for analyzing the whole spectrum of attractors-all saturated, all-but-one saturated, all-but-two saturated, etc. — of a neurodynamical system with a saturated sigmoidal function as its input-output characteristic. We present an argument which claims, under a mild condition, that only all saturated or all-but-one saturated attractors are observable for the neurodynamics. For any given all saturated configuration ξ (all-but-one saturated configuration η) the paper shows how to construct an exact parameter region R(ξ) (¯R(η)) such that if and only if the parameters fall within R(ξ) (¯R(η)), then ξ (η) is an attractor (a fixed point) of the dynamics. The parameter region for an all saturated fixed point attractor is independent of the specific choice of a saturated sigmoidal function, whereas for an all-but-one saturated fixed point it is sensitive to the input-output characteristic.
42776203
Fixed-Point Attractor Analysis for a Class of Neurodynamics
Nearly all models in neural networks start from the assumption that the input-output characteristic is a sigmoidal function. On parameter space, we present a systematic and feasible method for analyzing the whole spectrum of attractorsall-saturated, all-but-one-saturated, all-but-twosaturated, and so onof a neurodynamical system with a saturated sigmoidal function as its input-output characteristic. We present an argument that claims, under a mild condition, that only all-saturated or all but-one-saturated attractors are observable for the neurodynamics. For any given all-saturated configuration (all-but-one-saturated configuration ) the article shows how to construct an exact parameter region R()(()) such that if and only if the parameters fall within R()(()), then () is an attractor (a fixed point) of the dynamics. The parameter region for an all-saturated fixed-point attractor is independent of the specific choice of a saturated sigmoidal function, whereas for an all-but-one-saturated fixed point, it is sensitive to the input-output characteristic. Based on a similar idea, the role of weight normalization realized by a saturated sigmoidal function in competitive learning is discussed. A necessary and sufficient condition is provided to distinguish two kinds of competitive learning: stable competitive learning with the weight vectors representing extremes of input space and being fixed-point attractors, and unstable competitive learning. We apply our results to Linsker's model and (using extreme value theory in statistics) the Hopfield model and obtain some novel results on these two models.
10827335
A novel approach for analyzing dynamics in neural networks with saturated characteristics
Current theories about the dynamics of neural networks with nonlinear characteristics and parameterized by set of parameters are mostly based on approximations in one way or another. In this paper we first introduce a rigorous approach which allows us to check in which parameter region a given saturated state is an attractor of the dynamics: a saturated state w=(wi, i=1,...,N)∈{-1,1}N is an attractor of the dynamics if and only if there is a local field gap between neurons in J+(w)={i, wi=1} and J-(w)={i, wi=−1}. Then we apply the result to analyze several models in neural networks. In particular in the Hopfield model we calculate the capacity and give an exact relation between the capacity and the threshold.
9793653
Paradoxical Relationship between Output and Input Regularity for the FitzHugh-Nagumo Model
We examine the effects of changing the coefficient of variation (CV) of the inter-stimulus interval on the CV of the output interspike interval (ISI), using constant magnitude, supra-threshold point-process stimuli of the membrane potential variable in the FitzHugh-Nagumo model. The coefficient of variation of the input is changed within the context of a displaced exponential distribution. We find that for some values of mean inter-stimulus interval, CV of ISI has an inverse relationship with input coefficient of variation, whereas for other mean stimulation rates, CV of ISI increases with input coefficient of variation. Over a wide range of input regularity CV of ISI is approximately constant, and is less than 0.4.
2346851
CARE: An Automobile Crash Data Analysis Tool
The Critical Analysis Reporting Environment provides an efficient tool for transportation safer engineers and policymakers to use in analyzing the categorical crash data typically obtained from police reports. CAPE has proven successful in the traffic safety community for two reasons: its simplicity and its efficiency. It is currently being used in several states.
9882695
Rigorously Defining and Analyzing Medical Processes: An Experience Report
This paper describes our experiences in defining the processes associated with preparing and administrating chemotherapy and then using those process definitions as the basis for analyses aimed at finding and correcting defects. The work is a collaboration between medical professionals from a major regional cancer center and computer science researchers. The work uses the Little-JIL language to create precise process definitions, the Propel system to specify precise process requirements, and the FLAVERS system to verify that the process definitions adhere to the requirement specifications. The paper describes how these technologies were applied to successfully identify defects in the chemotherapy process. Although this work is still ongoing, early experiences suggest that this approach can help reduce medical errors and improve patient safety. The work has also helped us to learn about the desiderata for process definition and analysis technologies, both of which are expected to be broadly applicable to other domains.
1207779
Experience in Teaching a Management-Oriented Capstone Software Engineering Course
Many computer science departments teach a capstone software engineering course involving the design and implementation of a large-scale project. Such a course is normally taught with students working together in teams; consequently, students must face a number of problems related to project management and team dynamics. Our experience has been that students frequently fail to adequately cope with these organizational problems, and consequently fail to complete their projects at an acceptable level of quality. To prepare students to better face these problems, we have modified our capstone software engineering course to provide equal emphasis in both management and technical areas. Our approach to management topics is formal rather than anecdotal, stressing general principles applied to organizational problems arising specifically from situations that occur frequently in technical organizations. This paper reports on the structure of this course, and provides insight into our initial experience in teaching it.
27497470
OVERTURE: An Object-Oriented Framework for High Performance Scientific Computing
The Overture Framework is an object-oriented environment for solving PDEs on serial and parallel architectures. It is a collection of C++ libraries that enables the use of finite difference and finite volume methods at a level that hides the details of the associated data structures, as well as the details of the parallel implementation. It is based on the A++/P++ array class library and is designed for solving problems on a structured grid or a collection of structured grids. In particular, it can use curvilinear grids, adaptive mesh refinement and the composite overlapping grid methods to represent problems with complex moving geometry. This paper introduces Overture, its motivation, and specifically the aspects of the design central to portability and high performance. In particular we focus on the mechanisms within Overture that permit a hierarchy of abstractions and those mechanisms which permit their efficiency on advanced serial and parallel architectures. We expect that these same mechanisms will become increasingly important within other object-oriented frameworks in the future.
9973306
The Earth System Grid: Supporting the Next Generation of Climate Modeling Research
Understanding the Earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of 100 TB of simulation data and is growing rapidly. Looking toward mid-decade and beyond, we must anticipate and prepare for distributed climate research data holdings of many petabytes. The Earth System Grid (ESG) is a collaborative interdisciplinary project aimed at addressing the challenge of enabling management, discovery, access, and analysis of these critically important datasets in a distributed and heterogeneous computational environment. The problem is fundamentally a Grid problem. Building upon the Globus toolkit and a variety of other technologies, ESG is developing an environment that addresses authentication, authorization for data access, large-scale data transport and management, services and abstractions for high-performance remote data access, mechanisms for scalable data replication, cataloging with rich semantic and syntactic information, data discovery, distributed monitoring, and Web-based portals for using the system.