Volume 17 (2023)
Volume 16 (2022)
Volume 15 (2021)
Volume 14 (2020)
Volume 13 (2019)
Volume 12 (2018)
Volume 11 (2017)
Volume 10 (2016)
Volume 9 (2015)
Volume 8 (2014)
Volume 7 (2013)
Volume 6 (2012)
Volume 5 (2011)
Volume 4 (2010)
Volume 3 (2009)
Volume 2 (2008)
Volume 1 (2007)
Volume -2 (2005)
Volume -1 (2004)
Biological Systems Modeling
Weight synchronization in feedforward neural networks and a novel method to detect synchrony patterns

Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi

Volume 17, Issue 2 , September 2023, , Pages 100-110

  Neuronal synchronization as a significant cognitive phenomenon of the human brain, has attracted the interest of neuroscience researchers in recent years. This phenomenon is generally investigated in discrete and continuous neuronal models or experimentally recorded signals of the brain. In this study, ...  Read More

Neural Network / Biological & Artificial Neural Network / BNN & ANN
A Novel Neuronal Model based on Chaotic Behavior of Artificial Neural Networks

Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi

Volume 15, Issue 3 , December 2021, , Pages 199-209

  The presentation of new neuronal models to simulate cognitive phenomena in the brain has attracted the research interests in recent years. In this study, a new neural model based on the chaotic behavior of weights of artificial neural networks during training by back-propagation algorithm is presented. ...  Read More

Bioinformatics / Biomedical Informatics / Medical Informatics / Health Informatics
Dimensionality Reduction of Binding Site Sequence Data on Human Genome Using a Deep Autoencoder Neural Network

Hossein Bankikoshki; Seyed Ali Seyyedsalehi; Fatemeh Zare Mirakabad

Volume 11, Issue 3 , September 2017, , Pages 219-230

  The use of genomic nucleotide sequences as biochemical signals in machine learning methods is possible by converting these sequences into numerical codes. This conversion results in an unrealistic increase in the dimension of the data and encounters some data analysis operations such as visualization ...  Read More

Speech processing
New Biologically Inspired Connectionist Approaches To Improve Machine Speech Recognition

Mohammad Reza Yazdchi; Seyed Ali Seyed Salehi

Volume 1, Issue 3 , June 2007, , Pages 201-213

  One of the most important challenges in automatic speech recognition is in the case of difference between the training and testing data. To decrease this difference, the conventional methods try to enhance the speech or use the statistical model adaptation. Training the model in different situations ...  Read More