... Introduction: Brain Designs are Adaptive Designs 1 -- 1.2. ... ... Stages in the Adaptive Neural Computation 120 of a Vector Difference -- A. ... ... Adaptation To Strabismus Surgery 140 -- 5.5. ... ... Learning Neural Vectors and Adaptive Gains in a 275 -- Predictive Movement System -- 11.12. ... ... Coupled Vector and Adaptive Gain Learning 281 -- 11.14. ...
Advances in psychology ; 30
xvi, 336 stran : ilustrace ; 23 cm
- Conspectus
- Psychologie
- NML Fields
- oftalmologie
- psychologie, klinická psychologie
- NML Publication type
- kolektivní monografie
The neural network is computational model based on the features abstraction of biological neural systems. Th e neural networks have many ways of usage in technical fi eld. Th ey have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct soft ware agents or autonomous robots. In this paper is described usage of neural networks for ECG signal prediction. Th e ECG signal prediction can be used for automated detection of irregular heartbeat – extrasystole. Th e automated detection system of unexpected abnormalities is also described in this paper.
- MeSH
- Electrocardiography methods instrumentation utilization MeSH
- Cardiac Complexes, Premature diagnosis MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Neurons physiology MeSH
- Computing Methodologies MeSH
- Signal Processing, Computer-Assisted instrumentation MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
Štruktúry mozgového kmeňa a centrálne mechanizmy zodpovedné za generovanie kašľa sú stále nedostatočne objasnené. Len nedávno bol opísaný prvý model neurónovej siete zodpovednej za vytváranie kašľového vzoru. Podľa modelu eupnoický vzor dýchania a kašľový vzor sú generované tou istou sieťou respiračných neurónov uloženou v Botzingerovom komplexe a v rostrálnej časti ventrálnej respiračnej predĺženej miechy. Generátor dýchania vytvorí kašlový vzor po získaní informácie prichádzajúcej z kašľových interneurónov „druhého radu", uložených v oblasti nucleus tractus solitarius predĺženej miechy. Tieto interneuróny spracovávajú a prenášajú aferentné informácie z rýchlo sa adaptujúcich a z pomaly sa adaptujúcich receptorov dýchacích ciest. Navyše sa zdá, že ďalšie štruktúry mozgového kmeňa uložené v retikulárnej formácii Varolovho mosta, v rafeálnych jadrách predĺženej miechy a v priľahlom laterálnom tegmentálnom poli zohrávajú kľúčovú úlohu pri centrálnej integrácii kašľového reflexu.
The brain stem structures and central mechanisms involved in production of the cough reflex are still not understood satisfactorily. The first model of brain stem neuronal circuitry responsible for cough pattern production was proposed just recently. Its principal feature is, that both the eupnoeic breathing and the cough motor patterns are produced by the same neuronal respiratory network in the Botzinger complex/rostral ventral respiratory group of the medulla oblongata. Thus, central pattern generator of breathing is modified to produce cough by excitatory inputs from medullary nucleus tractus solitarii second-order cough interneurons, mediating afferent information from airway rapidly and slowly adapting receptors. Moreover, it seems that another brain stem structures localized in the reticular formation of pons Varoli, in the medullary raphe nuclei and the adjacent lateral tegmental field area play a key role in neuronal processing of the cough reflex.
- MeSH
- Afferent Pathways physiology MeSH
- Respiratory System innervation MeSH
- Respiratory Physiological Phenomena MeSH
- Cough MeSH
- Humans MeSH
- Medulla Oblongata anatomy & histology physiology MeSH
- Brain Stem anatomy & histology physiology MeSH
- Reflex MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Review MeSH
Brain-specific link protein Bral2 represents a substantial component of perineuronal nets (PNNs) enwrapping neurons in the central nervous system. To elucidate the role of Bral2 in auditory signal processing, the hearing function in knockout Bral2(-/-) (KO) mice was investigated using behavioral and electrophysiological methods and compared with wild type Bral2(+/+) (WT) mice. The amplitudes of the acoustic startle reflex (ASR) and the efficiency of the prepulse inhibition of ASR (PPI of ASR), produced by prepulse noise stimulus or gap in continuous noise, was similar in 2-week-old WT and KO mice. Over the 2-month postnatal period the increase of ASR amplitudes was significantly more evident in WT mice than in KO mice. The efficiency of the PPI of ASR significantly increased in the 2-month postnatal period in WT mice, whereas in KO mice the PPI efficiency did not change. Hearing thresholds in 2-month-old WT mice, based on the auditory brainstem response (ABR) recordings, were significantly lower at high frequencies than in KO mice. However, amplitudes and peak latencies of individual waves of click-evoked ABR did not differ significantly between WT and KO mice. Temporal resolution and neural adaptation were significantly better in 2-month-old WT mice than in age-matched KO mice. These results support a hypothesis that the absence of perineuronal net formation at the end of the developmental period in the KO mice results in higher hearing threshold at high frequencies and weaker temporal resolution ability in adult KO animals compared to WT mice.
- MeSH
- Acoustic Stimulation methods MeSH
- Time Factors MeSH
- Extracellular Matrix Proteins deficiency MeSH
- Adaptation, Physiological physiology MeSH
- Mice, Inbred C57BL MeSH
- Mice, 129 Strain MeSH
- Mice, Knockout MeSH
- Mice MeSH
- Nerve Net growth & development metabolism MeSH
- Peripheral Nerves growth & development metabolism MeSH
- Prepulse Inhibition physiology MeSH
- Nerve Tissue Proteins deficiency MeSH
- Evoked Potentials, Auditory, Brain Stem physiology MeSH
- Reflex, Startle physiology MeSH
- Age Factors MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.
- MeSH
- Water Cycle MeSH
- Humans MeSH
- Environmental Monitoring methods MeSH
- Neural Networks, Computer * MeSH
- Droughts * MeSH
- Forecasting MeSH
- Models, Theoretical * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this letter, we present a classification framework based on features extracted from magnetic resonance imaging (MRI) data using two automatic whole-brain morphometry methods: voxel-based (VBM) and deformation-based morphometry (DBM). The framework employs a random subspace ensemble-based artificial neural network classifier-in particular, a multilayer perceptron (MLP). The framework was tested on data from first-episode schizophrenia patients and healthy controls. The experiments differed in terms of feature extraction methods, using VBM, DBM, and a combination of both morphometry methods. Thus, features of different types were available for model adaptation. As we expected, the combination of features increased the MLP classification accuracy up to 73.12%-an improvement of 5% versus MLP-based only on VBM or DBM features. To further verify the findings, other comparisons using support vector machines in place of MLPs were made within the framework. However, it cannot be concluded that any classifier was better than another.
- MeSH
- Diagnosis, Computer-Assisted methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Brain diagnostic imaging MeSH
- Neural Networks, Computer * MeSH
- Pattern Recognition, Automated methods MeSH
- Schizophrenia diagnostic imaging MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feed-forward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on a large number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.
- MeSH
- Enzyme Activation MeSH
- Adaptation, Physiological MeSH
- Hypoxia MeSH
- L-Lactate Dehydrogenase MeSH
- Disease Models, Animal MeSH
- Brain metabolism MeSH
- Mice MeSH
- Neuronal Plasticity MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Comparative Study MeSH
