Search results for " Computer Science"
showing 10 items of 3983 documents
Study on the transient characteristic in the human visual system using masking experiments
1979
In this paper the visual masking effect is interpreted on the basic of the transient characteristic in two dimensional neuronal networks. The study investigates the suitability of the effect for use as a measurement method. It is shown that the stimulus distribution in space can be scanned at different points in time and that various dynamic characteristic values of the system can be measured.
Model predictions of the ionic mechanisms underlying the beating and bursting pacemaker characteristics of molluscan neurons
1976
The general properties of the excitable membrane on molluscan pacemaker neurons can be described on the basis of a fair amount of experimental evidence available in the literature. The neuronal membrane exhibits under voltage clamp an initial inward current carried by both Na+ and Ca2+ ions, the time- and voltage-dependent characteristics of which are similar to that of other excitable structures. The conductance mechanism for the two ion species and the transport kinetics appear to be closely similar. The time course and amplitude of the delayed outward current carried by K+ ions shows a marked dependence on the membrane potential. Characteristic for the molluscan neurons is the existence …
Neuronal network characteristics in the cat superior colliculus
1977
The system theoretical description of the superficial layers of neurons in the cat's superior colliculus is based on homogeneous linear space-time filters. The most important neurophysiological findings on the superior colliculus are simulated on the digital computer by generating suitable coupling functions and matching the space and time parameters. It is shown that the neurophysiological measurements can be interpreted by varying a few system parameters. The curves of velocity dependent responses, direction specificity and the effects of the colliculus specific surround are examined in particular. Computer simulation shows that such a surround can evaluate small moving stimuli differentl…
Multitasking associative networks.
2012
We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzman machine and we show its thermodynamical equivalence to an associative working memory able to retrieve multiple patterns in parallel without falling into spurious states typical of classical neural networks. We focus on systems processing in parallel a finite (up to logarithmic growth in the volume) amount of patterns, mirroring the low-level storage of standard Amit-Gutfreund-Sompolinsky theory. Results obtained trough statistical mechanics, signal-to-noise technique and Monte Carlo simulations are overall in perfect agreement and carry interesting biological insights. Indeed, these associative network…
Parameters analysis of FitzHugh-Nagumo model for a reliable simulation
2014
International audience; Derived from the pioneer ionic Hodgkin-Huxley model and due to its simplicity and richness from a point view of nonlinear dynamics, the FitzHugh-Nagumo model has been one of the most successful neuron / cardiac cell model. It exists many variations of the original FHN model. Though these FHN type models help to enrich the dynamics of the FHN model. The parameters used in these models are often in biased conditions. The related results would be questionable. So, in this study, the aim is to find the parameter thresholds for one of the commonly used FHN model in order to pride a better simulation environment. The results showed at first that inappropriate time step and…
Mutual-information based rate-adaptation for Multi-User TH-IR-UWB coded system
2011
In this paper we present a coding rate adaptation technique for a Time-Hopping Impulse-Radio Ultra-Wide Band (TH-IR-UWB) system assuming that the Multi-User Interference (MUI) is modeled as an additive interference noise following a Generalized Gaussian Distribution (GGD). The shape parameter induced by the GGD model is in general time-variant since it strongly depends on the essential UWB system parameters and the received signal power of the active users. In this paper, we show that the performance of a TH-IR-UWB LDPC coded system is quite independent of the GGD shape parameter when we consider the mutual information between the soft input to the decoder and the transmitted sequence, espe…
Quantification of melanin and hemoglobin in humain skin from multispectral image acquisition: use of a neuronal network combined to a non-negative ma…
2012
International audience; This article presents a multispectral imaging system which, coupled with a neural network-based algorithm, reconstructs reflectance cubes. The reflectance spectra are obtained using artificial neural-netwok reconstruction which generates reflectance cubes from acquired multispectral images. Then, a blind source separation algorithm based on Non-negative Matrix Factorization is used for the decomposition of human skin absorption spectra in its main pigments: melanin and hemoglobin. The analysis is performed on reflectance spectra. The implemented source separation algorithm is based on a multiplicative coefficient upload. The goal is to represent a given spectrum as t…
On Finite Satisfiability of Two-Variable First-Order Logic with Equivalence Relations
2009
We show that every finitely satisfiable two-variable first-order formula with two equivalence relations has a model of size at most triply exponential with respect to its length. Thus the finite satisfiability problem for two-variable logic over the class of structures with two equivalence relations is decidable in nondeterministic triply exponential time. We also show that replacing one of the equivalence relations in the considered class of structures by a relation which is only required to be transitive leads to undecidability. This sharpens the earlier result that two-variable logic is undecidable over the class of structures with two transitive relations.
Active Learning of Recursive Functions by Ultrametric Algorithms
2014
We study active learning of classes of recursive functions by asking value queries about the target function f, where f is from the target class. That is, the query is a natural number x, and the answer to the query is f(x). The complexity measure in this paper is the worst-case number of queries asked. We prove that for some classes of recursive functions ultrametric active learning algorithms can achieve the learning goal by asking significantly fewer queries than deterministic, probabilistic, and even nondeterministic active learning algorithms. This is the first ever example of a problem where ultrametric algorithms have advantages over nondeterministic algorithms.
Data-based modeling of vehicle collisions by nonlinear autoregressive model and feedforward neural network
2013
Vehicle crash test is the most direct and common method to assess vehicle crashworthiness. Visual inspection and obtained measurements, such as car acceleration, are used, e.g. to examine impact severity of an occupant or to assess overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using nonlinear autoregressive (NAR) model which parameters are estimated by the use of feedforward neural network. NAR model presented in this study is derived from the more general one - nonlinear autoregressive with moving average (NARMA). Suitability of autoregressive systems for data-based modeling was …