Sentence examples for model of tuning from inspiring English sources

In order to do this I first explore an approach to teaching auditory rhetoric based on ways of knowing sound from an acoustics and musicology perspective, then I consider a phenomenological approach based on listening, and finally I construct a model of "tuning the sonic playing field" that draws on the literal, material practice of tuning as a metaphor for how sound may be taught in composition.

We used a nonlinear quadratic model of tuning to express neuronal rates as a function of hand position and velocity.

While more sophisticated models of tuning, such as higher-order or non-parametric models, may model neural activity more accurately, our model is relatively simple, fast to fit and evaluate, and grounded in previous work (see Materials and Methods), while demonstrating significantly better predictive accuracy than the commonly-used linear model.

Moreover our study has to be thought of as the analog in the case of the structure tensor model to the analysis of tuning curves of the ring model of orientations (see [1, 2, 19, 35]).

This chapter reveals that the description of tuning database model is incomplete without the description of normalization.

In this paper, a discrete multi-degree of freedom model of a tuned disk is considered.

This filter modeled arm movement profiles better because it used the history of past movement, and it described neuronal modulations to movements better by using a quadratic model of neuronal tuning which included tuning at multiple time offsets.

The LDMvc is an improvement of the existing intersection-based LDM since the number of model tuning parameters is reduced to four through a sophisticated flow split algorithm, and a gas flow distribution model can be associated with the LDMvc.

It is not the scope of the presented model to tune synapses within a single inner hair cell to differentiate between high spontaneous and low spontaneous rate which can be elicited by the change of the synaptic ribbon [ 48].

One of the strengths of our approach is that the dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary.

To make easier the task of modeling, tuning and testing these retinal models, we have created a software tool that allows flexible and parametric definition and testing of retina-like models.