Neural oscillations, circular causality and the implications for nature



Neural network techniques in electromagnetic target classification: A comparison study
Sayan, Gönül (1999-01-01)
The performances of a self-organizing map classifier, a multilayer perceptron classifier and a conventional classifier, based on the well-known principal component analysis technique, are compared in classifying a group of model aircraft, according to their accuracy and their real-time classification speed
Neural network modeling of an ionospheric process: temporal and spatial forecasting of the critical frequencies
Kumluca, Ayça; Tulunay, Ersin; Department of Electrical and Electronics Engineering (1997)
Neural networks with piecewise constant argument and impact activation
Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011)
This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Neural-network quantum states for a two-leg Bose-Hubbard ladder under magnetic flux
Çeven, K.; Oktel, M. Ö.; Keleş, Ahmet (2022-12-01)
Quantum gas systems are ideal analog quantum simulation platforms for tackling some of the most challenging problems in strongly correlated quantum matter. However, they also expose the urgent need for new theoretical frameworks. Simple models in one dimension, well studied with conventional methods, have received considerable recent attention as test cases for new approaches. Ladder models provide the logical next step, where established numerical methods are still reliable, but complications of higher dim...
Neural networks and cascade modeling technique in system identification
Senalp, Erdem Turker; Tulunay, Ersin; Tulunay, Yurdanur (2006-01-01)
The use of the Middle East Technical University Neural Network and Cascade Modeling (METU-NN-C) technique in system identification to forecast complex nonlinear processes has been examined. Special cascade models based on Hammerstein system modeling have been developed. The total electron content (TEC) data evaluated from GPS measurements are vital in telecommunications and satellite navigation systems. Using the model, forecast of the TEC data in 10 minute intervals 1 hour ahead, during disturbed condition...
Citation Formats
T. E. Özkurt, “Neural oscillations, circular causality and the implications for nature,” 2018, Accessed: 00, 2021. [Online]. Available: