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In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multi-timescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.

The Minimum Description Length (MDL) criterion is used to fit a facet model of a car to an image. The best fit is achieved when the difference image between the car and the background has the greatest compression. MDL overcomes the overfitting and parameter precision problems which hamper the more usual maximum likelihood method of model fitting. Some preliminary results are shown.

Acetylcholinesterase (E.C. 3.1.1.7) is a typical hydrolase showing substrate inhibition. Classical works stated a two-step kinetics for the catalytic mechanism. We analyse here a full interacting model for the substrate bound to an inhibitory site different to the catalytic one, in agreement with current tridimensional data. A mixed model derived from equilibrium-steady state analysis fits fairly well to experimental data. We conclude that both catalytic steps are susceptible of partial inhibition, with 15% of remaining activity for substrate saturated enzyme. Indeed, substrate inhibition would be preferentially exerted on one of the catalytic steps, leading to particular cases which fit to data as well. The parametric identity among the mathematical expressions from restricted models disables identifiability of kinetic constants solely by fitting to data. Thus, additional information is required from other experimental approaches. Based on the cited closeness between both rate constants for the substrate acetylthiocholine, dissociation constants at the catalytic and inhibitory sites are estimated in ~ 63 μM and ~ 0.35 mM respectively, while interaction between binding sites might enhance the latter up to 16 mM. We also emphasise the relevance of considering substrate inhibition in kcat extrapolations even at low substrate concentrations. Finally, we are able to predict total acetylated enzyme values in the order of those done from direct experimental measurements.

In this paper, we propose a novel, analytically tractable, one-factor stochastic model for the dynamics of credit default swap (CDS) spreads and their returns, which we refer to as the spread-return mean-reverting (SRMR) model. The SRMR model can be seen as a hybrid of the Black–Karasinski model on spreads and the Ornstein–Uhlenbeck model on spread returns, and is able to capture empirically observed properties of CDS spreads and returns, including spread mean-reversion, heavy tails of the return distribution, and return autocorrelations. Although developed for modeling CDS spreads, the SRMR model has applications for many other stochastic processes with similar empirical properties, including more general rate processes.

We present a package (Astronomical Image Decomposition and Analysis, AIDA), developed in IDL, specifically designed to perform 2D model fitting of quasar images. The software provides simultaneous decomposition into nuclear and host components. AIDA is a “user-friendly” software, which can work both in “interactive” and “batch” mode and manage complex PSF models (including HST and AO psf), and allows one to characterize its spatial variability in the field.

The Minimum Description Length (MDL) criterion is used to fit a facet model of a car to an image. The best fit is achieved when the difference image between the car and the background has the greatest compression. MDL overcomes the overfitting and parameter precision problems which hamper the more usual maximum likelihood method of model fitting. Some preliminary results are shown.