computation
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elektronický časopis
- MeSH
- matematické výpočty počítačové MeSH
- modely genetické MeSH
- teoretické modely MeSH
- výpočetní biologie MeSH
- Konspekt
- Biologické vědy
- NLK Obory
- biologie
- biologie
- biomedicínské inženýrství
- NLK Publikační typ
- elektronické časopisy
elektronický časopis
- Konspekt
- Psychologie
- NLK Obory
- psychologie, klinická psychologie
- NLK Publikační typ
- elektronické časopisy
sv.
- MeSH
- neuronové sítě MeSH
- Publikační typ
- periodika MeSH
- Konspekt
- Automatizační a řídicí technika
- NLK Obory
- neurovědy
- technika
Background: Quantitative structure–activity relationships (QSAR) are a major factor in contemporary drug designing. Thus, it is quite clear that a large number of users of QSAR are located in industrial research units. Objectives: A Topological Index is a numeric quantity that is mathematically derived in a direct and uambiguous manner from the structural graph of a molecule. In structure-activity relationship studies, molecular topology quantifies chemical structure into characteristic numerical descriptors. All structural formulas of chemical compounds are molecular graphs where vertices represent the set of atoms and edges represent chemical bonds. The construction and investigation of topological indices that could be used to describe molecular structures is one of the important directions of mathematical chemistry Topological descriptors developed for predicting physicochemical properties and biological activities of chemical substances can be used for drug design. Matherials and Methods: A number of successful QSAR studies were made based on the Wiener index, Terminal Wiener Index and Platt Number. These indices are derived from matrices, like distance matrix and adjacency matrix which represents a molecular graph. Zagreb Index is based on degree connectivity indices. Results and Conclusion: In this paper we analyze, Quantitative structure activity relationship studies were performed on anti- HIV activity of Quinolone carboxylic acid for Wiener Index, Terminal Wiener Index, Platt Number and Zagreb Index.
OBJECTIVE: This paper aims to improve the shortcomings of the extant methodologies for realistic Laplacian (RL) computation, and correct the erroneous claims published in the past. METHODS: We implemented several variants of RL computation methods, using various potential approximation techniques and different regularization approaches. The individual variants of the RL computation were tested using simulations based on a realistic head model computed with the boundary element method (BEM). The results which disagreed with previously published works were further analyzed, and the reasons for the disagreement were identified. RESULTS: We identified the best regularization techniques for the surface potential approximation, and we showed that once these techniques are used there is often little difference between various potential approximations, which is in contrast with previous claims that promoted the radial basis function (RBF) approximation. Further, our analysis shows that the RBF approximation suffers from Runge phenomenon, which cannot be mitigated simultaneously for both deep and shallow sources; therefore, its good performance is guarantied only if a priori knowledge about the source depth is available. CONCLUSIONS: The previously published methodology for RL computation was not optimal. Improvements are possible if the newly suggested approach is used. SIGNIFICANCE: The methodology presented in our paper allows more efficient utilization of the RL, providing a useful tool for processing of high density EEG recordings. Presented techniques allow to achieve high EEG spatial resolution, and avoid unnecessary spatial blurring caused by the problems in the previously published RL methodology.
This paper explores regularization options for the ill-posed spline coefficient equations in the realistic Laplacian computation. We investigate the use of the Tikhonov regularization, truncated singular value decomposition, and the so-called lambda-correction with the regularization parameter chosen by the L-curve, generalized cross-validation, quasi-optimality, and the discrepancy principle criteria. The provided range of regularization techniques is much wider than in the previous works. The improvement of the realistic Laplacian is investigated by simulations on the three-shell spherical head model. The conclusion is that the best performance is provided by the combination of the Tikhonov regularization and the generalized cross-validation criterion-a combination that has never been suggested for this task before.
svazky
elektronický časopis
- MeSH
- chemie metody MeSH
- fyzikální chemie MeSH
- Konspekt
- Chemie. Mineralogické vědy
- NLK Obory
- chemie, klinická chemie
- NLK Publikační typ
- elektronické časopisy
In neural computation, the essential information is generally encoded into the neurons via their spiking configurations, activation values or (attractor) dynamics. The synapses and their associated plasticity mechanisms are, by contrast, mainly used to process this information and implement the crucial learning features. Here, we propose a novel Turing complete paradigm of neural computation where the essential information is encoded into discrete synaptic states, and the updating of this information achieved via synaptic plasticity mechanisms. More specifically, we prove that any 2-counter machine-and hence any Turing machine-can be simulated by a rational-weighted recurrent neural network employing spike-timing-dependent plasticity (STDP) rules. The computational states and counter values of the machine are encoded into discrete synaptic strengths. The transitions between those synaptic weights are then achieved via STDP. These considerations show that a Turing complete synaptic-based paradigm of neural computation is theoretically possible and potentially exploitable. They support the idea that synapses are not only crucially involved in information processing and learning features, but also in the encoding of essential information. This approach represents a paradigm shift in the field of neural computation.
