Neural modeling

Bhat, N. V., and McAvoy, T. J., Determining the structure for neural models by network stripping. Comput. Chem. Eng. 16, 271 (1992). 

There has been a steady development of neuronal analogs over the past 50 years. An important early model was proposed in 1943 by McCulloch and Pitts [23]. They described the neuron as a logical processing unit, and the influence of their model set the mathematical tone of what is being done today. Adaption or learning is a major focus of neural net research. The development of a learning rule that could be used for neural models was pioneered by Hebb, who proposed the famous Hebbian model for synaptic modification [24]. Since then, many alternative quantitative interpretations of synaptic modification have been developed [15-22]. 

Additional neural models for color constancy, which were not discussed in depth, were proposed by Courtney et al. (1995). 

Devillers, J. 1993. Neural modelling of the biodegradability of benzene derivatives. SAR and QSAR in Environ. Res. 1 161-167. 

Neural Modeling and Functional Brain Imaging The Interplay Between the Data-Fitting and Simulation Approaches... 

Wehner, R. (1987). Matched filters - neural models of the external world../. Comp. Physiol. A, 161, 511-531. 

Cottrell, M Girard, B., Girard, Y., Mangeas, M. Muller, C. (1995). Neural modeling for time series a statistical stepwise method for weight elimination. IEEE Transactions on Neural Networks 6,1355-1364. 

Key words Chemogenomics, Biological target, Neural modeling, Kohonen self-organized maps, GPCR, Compound library, Chemokine, Receptor... 

Calculation and Mapping neural model was loaded and the appropriate descriptors were... 

Vohradsky, J. 2001. Neural model of the genetic network. J Biol Chem 276 36168-73. 

F. Hoppenstead and E.M. Izhikevich, Canonical neural models. Brain Theory and Neural Networks, 2 Ed., MIT Press, Cambridge, MA, (2002). 

B. Hauschildt, A. G. Balanov, N. B. Janson, and E. Scholl Control of noise-induced cooperative dynamics in coupled neural models, Phys. Rev. E 74, 051906 (2006). 

D. Golomb, D. Hansel, and G. Mato. Mechanisms of synchrony of neural activity in large networks. In F. Moss and S. Gielen, editors. Neuro-informatics and Neural Modeling, volume 4 of Handbook of Biological Physics, pages 887-968. Elsevier, Amsterdam, 2001. 

TABLE 2. Effects of Organophosphonis Compounds on jfi Vitro Neural Models... 

Sejnowski, T.J. and Tesauro, G. 1989. The Hebb rule for synaptic plasticity algorithms and implementations. In JH Byrne and WO Berry (Eds.), Neural Models of Plasticity, pp. 94—103, New York, Academic Press. 

Abbas, J.J. 1995. Using neural models in the design of a movement control system. In Computational Neuroscience. J.M. Bower, Ed. pp. 305—310. Academic Press, New York. 

K. Ciesielski and I. Zbicinski, Hybrid neural modelling of fluidised bed drying process. Drying Technology, 19(8) 1725-1738 (2001). 

Regarding handling of model responses, process inversion (calculation of u°p with the help of the model) can be performed implicitly with the help of numerical procedures (the model provides process responses y as functions of inputs u and initial states x), or can be performed explicitly, by developing empirical and/or hybrid neural models off-line (the model provides inputs u as functions of process responses y and initial states x) [ 196, 203-206]. In the first case, model responses are more robust, although model inversion is much faster in the second case. Besides, if the process model can be fairly described by linear or bilinear models, then analytical results can be provided for the optimization problem [40,193,207,208], which makes the real-time implementation of predictive controllers much easier. 

J. Anderson, J. Silverstein, S. Ritz, and R. Jones, Psychol. Rev., 84, 413 (1977). Distinctive Features, Categorical Perception, and Probability Learning Some Applications of a Neural Model. 

C. Munoz-Caro and A. Nino, Comput. Chem., 22,355 (1998). Neural Modeling of Torsional Potential Hypersurfaces in Non-Rigid Molecules. 

Fogel DB, Wasson EC, Boughton EM et al (1998) Linear and neural models for classifying breast masses. IEEE Trans Med Imaging 17 485-488... 

Summary. It is shown, that in complex chemical reaction systems a very high redundancy in the parameter space of kinetic rate constants occurs which renders the determination of kinetic data difficult or often impossible. Two methods which overcome this parameter redundancy are presented. In the first procedure effective parameters are locally defined and adapted during a standard optimization procedure. The second method approximates the kinetic behaviour of measured concentrations with a neural network. Both methods are analysed on the basis of an example reaction for the neural modelling we present also numerical results. 

An alternative approach for modelling chemical kinetics can be realized by the use of neural networks which serve as universal approximators (Hertz el al [4]). Neural models describe only relations between measurable quantities which in the case of the example above can be the slow-varying concentration of hydrobromic acid. Within the framework of neural networks we have to model the relationship between concentration and reaction rate of hydrobromic acid... 

Left Comparison between the simulated time series (o) and the neural model (—). Right Theoretical effective kinetics of Eq. (3.14) (-. ) versus approximation by a neural network Eq. (4.1) (—) fitted from simulated concentration time series. 

Neural Modeling Seventh International Conference, IWANN 2003, Mao, Spain, June 3-6, 2003, Proceedings, Vol. 2687, J. M6ra and J. R. Alvarez, Eds., Springer-Verlag, New York, 2003, pp. 798-805. Feature Reduction Using Support Vector Machines for Binary Gas... 

An alternative way of solving this difficulty is the use of dynamical models, based on combinations of first principles and neural networks (NN), called grey-box neural models (GNM). A GNM normally consists of a phenomenological part (heat and/or mass balances differential equations) and an empirical part (a neural network in this work). Due to the inherent flexibility of NN, models based on this structure are well suited to represent complex functions such as those encountered in chemical reaction processes. This work proposes incorporate in the RTO system a dynamical GNM of the... 

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