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See [6] for introduction to mathematical models in biological sciences.
In recent years, the almost periodic solution of the models in biological populations has been studied extensively (see [11 18] and the references cited therein).
Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision.
Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task.
The model description language allows users to describe their models in biological and mathematical terms.
It allows rapid development of multiscale models in biological terms and mathematical expressions rather than programming code.
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Some perspectives are also given on the future trends of computational modeling in biological thermodynamics.
These findings provide concrete predictions for physiological and psychophysical experiments to validate our model in biological cognitive systems.
In this review, we provide a brief history and highlight some of the important applications of modeling in biological systems with an emphasis on the study of excitable cells.
Network modeling in biological systems [ 2, 8, 11] stresses the relative importance of highly connected nodes.
While this definition of a scientific model is more in line with modeling in biological systems, it is insufficient to shape how models can be used in a classroom.
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