Biological Systems Modeling
Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi
Volume 17, Issue 2 , September 2023, , Pages 100-110
Abstract
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, ...
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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, for the first time, we investigate the weight synchronization instead of neuronal synchrony, in the training step of the artificial feedforward neural networks. The findings of the study show that the generalized weight synchronization occurs both during the training mode and in the non-training mode. Furthermore, as the training is completed, the synchronization increases between the weights. In this study, a new method is introduced in order to detect synchrony patterns using signal derivative and hierarchical clustering. We have also presented a criterion to quantify weight synchronization in different layers of the neural network. Accordingly, the results demonstrate that the lower layers of the network have a significantly higher level of weight synchrony than the upper layers.
Neural Network / Biological & Artificial Neural Network / BNN & ANN
Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi
Volume 15, Issue 3 , December 2021, , Pages 199-209
Abstract
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. ...
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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. This model is the first discrete neuronal model with learning ability and shows complex and chaotic behaviors. The learning ability of this model has enabled it to simulate cognitive phenomena such as neuronal synchronization in near-realistic conditions. The model, which is derived from a simple three-layered feed-forward neural network, has several coexisting attractors that make learning possible in various basins of attraction. The study of model parameters shows that bifurcation occurs not only by changing the learning rate, but also external stimulation can change the model behavior and bifurcation pattern. This point that can be used in modeling and designing new therapies for cognitive disorders.
Bioinformatics / Biomedical Informatics / Medical Informatics / Health Informatics
Hossein Bankikoshki; Seyed Ali Seyyedsalehi; Fatemeh Zare Mirakabad
Volume 11, Issue 3 , September 2017, , Pages 219-230
Abstract
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 ...
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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 and feature extraction with constraints. Therefore, one should use the dimensionality reduction technics in order to return the data to its real dimension. In this study, a deep autoencoder neural network has been used to reduce the dimension of binding site sequence data on the human genome. In order to determine whether the information of real data is preserved in compressed data, we perform a two-class classification using a support vector machine. The results show that information is almost entirely preserved in compression. Then, compressed data is used for visualization as well as feature selection by analysis of variance. The results show that the first, the tenth and eighth positions in the sequences are the most informative positions. While the majority of the previous works deal with gene expression data of microarrays and compare a few dimension reduction algorithms, this paper for the first time uses an autoencoder on nucleotide sequence data and provides a comprehensive comparison between the performance of the dimension reduction technics and machine learning algorithms.
Speech processing
Mohammad Reza Yazdchi; Seyed Ali Seyed Salehi
Volume 1, Issue 3 , June 2007, , Pages 201-213
Abstract
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 ...
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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 is another example of these methods. The success rate in these methods compared to those of cognitive and recognition systems of human beings seems too much primary. In this paper, an inspiration from human beings' recognition system helped us in developing and implementing a new connectionist lexical model. Integration of imputation and classification in a single NN for ASR with missing data was investigated. This can be considered as a variant of multi-task learning because we train the imputation and classification tasks in parallel fashion. Cascading of this model and the acoustic model corrects the sequence of the mined phonemes from the acoustic model to the desirable sequence. This approach was implemented on 400 isolated words of TFARSDAT Database (Actual telephone database). In the best case, the phoneme recognition correction increased in 16.9 percent. Incorporating prior knowledge (high level knowledge) in acoustic-phonetic information (lower level) can improve the recognition. By cascading the lexical model and the acoustic model, the feature parameters were corrected based on the inversion techniques in the neural networks. Speech enhancement by this method had a remarkable effect in the mismatch between the training and testing data. Efficiency of the lexical model and speech enhancement was observed by improving the phonemes' recognition correction in 18 percent compared to the acoustic model.
