Network model
Dotaz
Zobrazit nápovědu
Teorie nelineárních dynamických systémů aplikovaná na biologické neuronové sítě, u nichž je prokázáno deterministicky chaotické chování, vysvětluje překvapivým způsobem ukládání a rychlé vybavovaní paměťových stop. Pomocí fázových přechodů a synchronizace oscilací lze interpretovat epileptickou aktivitu jako projev univerzálních vlastností deterministicky chaotických systémů. Modely mozkové aktivity založené na chaotických neuronech lépe vystihují skutečné chování biologických neuronů.
The theory of non-linear dynamic systems applied to biological neurone networks where a determinist chaotic behaviour was proved explains in a surprising way the deposition and rapid recollection of memory traces. By means of phasic transition and synchronization of oscillations it is possible to interpret the epileptic activity as a manifestation of universal properties of determinist chaotic systems. Models of cerebral activity based on chaotic neurones give a better idea of the actual behaviour of biological neurones.
- MeSH
- modely neurologické MeSH
- neuronové sítě MeSH
- stochastické procesy MeSH
- Publikační typ
- přehledy MeSH
Sport v sobě zahrnuje polyfunkční společenský jev, ve kterém nelze nevidět vztahy sportu k ostatním oblastem společenské činnosti. V našem příspěvku jsme se zaměřili na provázanost sportu s ekonomikou, kde poukazujeme zejména na financování sportovních klubů. Tyto finanční zdroje se dotýkají sportovních neziskových organizací a jsou naznačeny na Andreffových modelech. Dalším modelem, který navazuje je hospodářský model neziskové organizace, který rozšiřuje informace o příjmech sportovních klubů v České republice.
Sport involves multifunctional social phenomenon. There is relationship to other areas of social activities in sport. In our report we focused on relation between sport and economics, specially financing sports clubs. These financial resources touch on non profit making organization and they down in Andreff´s models. Further follow-up model is economic model of non profit making organization. That model expands information about incomes of sports clubs in Czech Republic. We suppose that differenciations of financing exist at sport clubs in Czech Republic and in abroad. They do not concern about club size but about legal form. The goal of this article is chracteristic and comparision. The goal was done by using the methods of documents working, analyse, synthesis and comparision.
- Klíčová slova
- sport, modely, neziskové organizace, financování.,
- MeSH
- ekonomické modely MeSH
- lidé MeSH
- neziskové organizace ekonomika MeSH
- spolupráce organizací a občanů ekonomika MeSH
- sporty ekonomika MeSH
- tělesná výchova ekonomika MeSH
- Check Tag
- lidé MeSH
BACKGROUND: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. RESULTS: Here we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. CONCLUSIONS: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.
- MeSH
- genomika MeSH
- neuronové sítě * MeSH
- sekundární struktura proteinů MeSH
- strojové učení * MeSH
- Publikační typ
- časopisecké články MeSH
64 s. : il., tab. ; 30 cm
1 online zdroj
- MeSH
- mozek fyziologie MeSH
- nervová síť * MeSH
- neurovědy MeSH
- počítačová simulace MeSH
- Publikační typ
- periodika MeSH
- Konspekt
- Lékařské vědy. Lékařství
- NLK Obory
- neurovědy
- MeSH
- expertní systémy MeSH
- financování organizované MeSH
- lékařská počítačová informatika využití MeSH
- lidé MeSH
- počítačové komunikační sítě využití MeSH
- programovací jazyk MeSH
- směrnice pro lékařskou praxi jako téma normy MeSH
- teoretické modely MeSH
- uživatelské rozhraní počítače MeSH
- využití lékařské informatiky MeSH
- Check Tag
- lidé MeSH
This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.
- MeSH
- algoritmy MeSH
- expertní systémy * MeSH
- fuzzy logika MeSH
- lidé MeSH
- neuronové sítě MeSH
- neurorehabilitace * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
