13913953
Random pulse input versus continuous current plus white noise: Are they equivalent?
Abstract We compared the behavior of the Hodgkin–Huxley (HH) model subjected to two input regimes: firstly, discrete pulses from a Poisson process, and then to a diffusion approximation, constructed to share statistical properties with the discrete pulse input. The results show that under a wide range of physiologically plausible input conditions the diffusion process is equivalent to the pulse process, in terms of output interspike interval distribution. In this respect, the HH model differs from the integrate and fire model, which has been widely used to address a variety of questions in computational neuroscience.
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.
7897839
Effects of Correlation and Degree of Balance in Random Synaptic Inputs on the Output of the Hodgkin-Huxley Model
We examine the effects of degree of balance between inhibitory and excitatory random synaptic inputs, and of positive correlation between the inputs on the mean and variability of the output of the classical Hodgkin-Huxley (HH) model for squid giant axon, using computer simulation. The mean interspike interval (ISI) and the coefficient of variation of ISI change little as the degree of balance changes, unlike the leaky integrate-and-fire model, frequently used in stochastic network modelling as an approximation to more biophysically based models. Low correlations (up to about 0.1) between 100 excitatory inputs each firing at 100 Hz reduce the mean(ISI) to below a third of its value when the inputs are independent, and CV by a factor of 5 from a near-Poisson range to one associated with regular firing.
877766
Inhibitory inputs increase a neurons's firing rate
Abstract Neurons are typically thought of as receiving information primarily through excitatory inputs, with inhibitory inputs playing a gating or regulating role. In this paper we demonstrate that increasing the strength of inhibitory inputs to the Hodgkin–Huxley and FitzHugh–Nagumo models can induce them to fire faster. This result is counter-intuitive and important in neural network modelling where inhibitory inputs are often neglected.
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.
18397185
Motor Fault Diagnosis Based on Wavelet Energy and Immune Neural Network
Motor fault diagnosis methods are crucial in acquiring safe and reliable operation in motor drive systems. In this paper, a new method for the motor fault diagnosis is proposed based on wavelet packet transform (WPT) and artificial neural network (ANN). The energy of the vibration signals of motor can be obtained by the multi-decomposition of WPT and used as feature values of ANN inputs for fault diagnosis system. The artificial immune algorithm (AIA) for data clustering is employed to adaptively choose the centers and widths of the hidden layer centers of the radial basis function neural network (RBFNN). The simulation experiment results show the applicability and effectiveness of the proposed method to motor fault diagnosis.
10058829
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation.
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.
60537960
An orthodontic expert system
Abstract A Fuzzy Relational Inference Language, FRIL has been used to generate an expert system to help non-specialist dentists with orthodontic problems. The use of fuzzy relations, descriptors and numbers has been found useful in modelling the thought processes of orthodontic specialists.
16581818
A Case Study of Software Process Improvement During Development
We present a case study of the use of a software process improvement method which is based on the analysis of defect data. The first step of the method is the classification of software defects using attributes which relate defects to specific process activities. Such classification captures the semantics of the defects in a fashion which is useful for process correction. The second step utilizes a machine-assisted approach to data exploration which allows a project team to discover such knowledge from defect data as is useful for process correction. We show that such analysis of defect data can readily lead a project team to improve their process during development. >