We exploit the dense structure of nuclei to postulate that in such clusters, the neuronal cells
will communicate via soma-to-soma interactions, aswell as through synapses. Using the …

This paper first proposes a new type of single-output Chebyshev-polynomial feed-forward
neural network (SOCPNN) for pattern classification. A new type of multi-output Chebyshev …

Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications
introduces the outlook and extension toward deep neural networks, with a focus on the …

Wine, an alcoholic beverage made from fermented grapes, has become an increasingly
popular drink. However, wine regions may directly affect the quality and taste of the wine …

Artificial neuronets (AN), especially with error back-propagation (BP) training algorithms,
have been widely investigated and applied in various science and engineering fields …

Recent work demonstrated the value of multi clusters of spiking Random Neural Networks
(RNN) with dense soma-to-soma interactions in deep learning. In this paper we go back to …

Breast cancer has become one of the leading causes of death in female population due to
its high morbidity and mortality. However, the treatment options for benign or malignant …

We assume that, within the dense clusters of neurons that can be found in nuclei, cells may
interconnect via soma-to-soma interactions, in addition to conventional synaptic …

An earthquake-forecasting attempt is presented in this work via combining the weights and
structure policy (WASP) and addition-subtraction frequency (ASF) algorithms. Specifically …

The random neural network (RNN) is a mathematical model for an" integrate and fire"
spiking network that closely resembles the stochastic behaviour of neurons in mammalian …