scope and structure of computational neuroscience.Synaptic-level structure is addressed in chapters that relate the properties of dendritic branches, spines, and synapses to the biophysics of computation and provide a connection between real neuron architectures and neural network simulations.The network-...
(CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge ...
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applications in realistic chemistry problems
J.: An artificial neural-network hourly temperature forecaster with applications in load forecasting, IEEE Trans. Power Systems 11(3) (1996), 870-876. Google Scholar Khotanzad, A., Hwang, R. C., Abaye, A., and Maratukulam, D.: An adaptive modular artificial neural network hourly load...
1 2 COMPUTATIONAL AND NEURAL NETWORK MODDominique ValentinBetty EdelmanHerv E AbdiValentin, D., Edelman, B., & Abdi, H. (1998). Computational and Neural network models of face processing. Journal of Biological System, 6, 213-219.Computational and Neural network models of face processing - ...
Computational models of retinal function; Retinal network models Definition Computational models of the neural retina simulate the response of the retina to input light. In their most detailed form, the models yield neural output as a spatially varying pattern of spike trains which fully encode the ...
T. Neural basis of category-specific semantic deficits for living things: evidence from semantic dementia, HSVE and a neural network model. Brain 130, 1127–1137 (2007). Article PubMed Google Scholar Eggert, G. H. Wernicke's Works on Aphasia: a Source-Book and Review (Mouton, 1977). ...
Deep learning: a machine learning method based on artificial neural networks, which learns complex patterns and features through multi-layer data representation and processing; LR: logistic regression; ANN: artificial neural network; SVM: support vector machine; RF: random forest; DNN: deep neural ...
2.1.4Recurrent neural networks Arecurrentneural networkarchitecture is different from a feed-forward neural network in that it has at least one feedback loop (Haykin, 2009). In consequence, a neuron receives inputs both externally from network inputs and internally from feedback loops. Perhaps th...
In this work we address this question by trying to understand how powerful and trainable quantum machine learning models are in relation to popular classical neural networks. We propose the effective dimension—a measure that captures these qualities—and prove that it can be used to assess any ...