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- ItemA computational neurogenetic model of a spiking neuron(IEEE, 2005) Kasabov, N; Benuskova, L.; Wysoski, S.The paper presents a novel, biologically plausible spiking neuronal model that includes a dynamic gene network. Interactions of genes in neurons affect the dynamics of the neurons and the whole network through neuronal parameters that change as a function of gene expression. The proposed model is used to build a spiking neural network (SNN) illustrated on a real EEC data case study problem. The paper also presents a novel computational approach to brain neural network modeling that integrates dynamic gene networks with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network through neuronal parameters, which are no longer constant, but change as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and ANN parameters, particular target states of the neural network operation can be achieved, and statistics about gene intercation matrix can be extracted. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). The behavior of SNN is evaluated by means of the local field potential, thus making it possible to attempt modeling the role of genes in different brain states, where EEC data is available to test the model. We use standard signal processing techniques like FFT to evaluate the SNN output to compare it with real human EEC data. © 2005 IEEE.
- ItemA novel microarray gene selection method based on consistency(IEEE Computer Society Press, 2006) Hu, Y; Pang, S; Havukkala, IConsistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, we address this issue as a consistency problem. We propose a new concept of performance-based consistency and a new novel gene selection method, Genetic Algorithm Gene Selection method in terms of consistency (GAGSc). The proposed consistency concept and GAGSc method were investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy. More importantly, GAGSc has demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.
- ItemA PSO based adaboost approach to object detection(Springer Verlag, 2008) Mohemmed, AW; Zhang, M; Johnston, MThis paper describes a new approach using particle swarm optimisation (PSO) within AdaBoost for object detection. Instead of using the time consuming exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two PSO based methods in this paper. The first uses PSO to evolve and select the good features only and the weak classifiers use a kind of decision stump. The second uses PSO for both selecting the good features and evolving weak classifiers in parallel. These two methods are examined and compared on a pasta detection data set. The experiment results show that both approaches perform quite well for the pasta detection problem, and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only for this problem.
- ItemA two-stage methodology for gene regulatory network extraction from time-course gene expression data(IEEE, 2004) Chan, Z.; Kasabov, N; Collins, L.The discovery of gene regulatory networks (GRN) from time-course gene expression data (gene trajectory data) is useful for (1) identifying important genes in relation to a disease or a biological function; (2) gaining an understanding on the dynamic interaction between genes; (3) predicting gene expression values at future time points and accordingly, (4) predicting drug effect over time. In this paper, we propose a two-stage methodology that is implemented in the software "Gene Network Explorer (GNetXP)" for extracting GRNs from gene trajectory data. In the first stage, we apply a hybrid Genetic Algorithm and Expectation Maximization algorithm on clustering the large number of gene trajectories using the mixture of multiple linear regression models for fitting the trajectory data. In the second stage, we apply the Kalman Filter to identify a set of first-order differential equations that describe the dynamics of the representative trajectories, and use these equations for discovering important gene interactions and predicting gene expression values at future time points. The proposed method is demonstrated on the human fibroblast response gene expression data. ©2004 IEEE.
- ItemA versatile quantum-inspired evolutionary algorithm(IEEE, 2007-09-25) Platel, M.; Sehliebs, S.; Kasabov, NThis study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called Versatile Quantum-inspired Evolutionary Algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a Classical Genetic Algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to Estimation of Distribution Algorithms (EDA). © 2007 IEEE.
- ItemAn adaptive model of person identification combining speech and image information(IEEE, 2004) Zhang, D.; Ghobakhlou, A.; Kasabov, NThe paper introduces a combination of adaptive neural network systems and statistical method for integrating speech and face image information for person identification. The method allows for the development of models of persons and their on-going adjustment based on new speech and face images. The method is illustrated with a modeling and classification of different persons, when speech and face images are presented in an incremental way. In this model, there are two sub - networks, one for face image and one for speaker recognition. A higher-level layer is applied to make a final decision. In the speaker recognition subnetwork, a text-dependant model is built using Evolving Connectionist Systems (ECOS) . In the face image recognition sub-network, composite profile technique is applied for face image feature extraction and Zero Instruction Set Computing (ZISC)  technology is used to build the neural network. In the higher-level conceptual subsystem, final recognition decision is made using statistical method. The experiments show that ECOS and ZISC are appropriate techniques for the creation of evolving models for the task of speaker and face recognition individually. It is also shown that the integration of the speech and image information using statistical method improves the person identification rate. © 2004 IEEE.
- ItemAn incremental principal component analysis for chunk data(IEEE, 2006) Ozawa, S.; Pang, S.; Kasabov, NThis paper presents a new algorithm of dynamic feature selection by extending the algorithm of Incremental Principal Component Analysis (IPCA), which has been originally proposed by Hall and Martin. In the proposed IPCA, a chunk of training samples can be processed at a time to update the eigenspace of a classification model without keeping all the training samples given so far. Under the assumption that L of training samples are given in a chunk, first we derive a new eigenproblem whose solution gives us a rotation matrix of eigen-axes, then we introduce a new algorithm of augmenting eigen-axes based on the accumulation ratio. We also derive the one-pass incremental update formula for the accumulation ratio. The experiments are carried out to verify if the proposed IPCA works well. Our experimental results demonstrate that it works well independent of the size of data chunk, and that the eigenvectors for major components are obtained without serious approximation errors at the final learning stage. In addition, it is shown that the proposed IPCA can maintain the designated accumulation ratio by augmenting new eigen-axes properly. This property enables a learning system to construct an informative eigenspace with minimum dimensionality. © 2006 IEEE.
- ItemAre probabilistic spiking neural networks suitable for reservoir computing?(AUT University, 2011-07-31) Schliebs, S; Mohemmed, A; Kasabov, NThis study employs networks of stochastic spiking neurons as reservoirs for liquid state machines (LSM). We experimentally investigate the separation property of these reservoirs and show their ability to generalize classes of input signals. Similar to traditional LSM, probabilistic LSM (pLSM) have the separation property enabling them to distinguish between different classes of input stimuli. Furthermore, our results indicate some potential advantages of non-deterministic LSM by improving upon the separation ability of the liquid. Three non-deterministic neural models are considered and for each of them several parameter configurations are explored. We demonstrate some of the characteristics of pLSM and compare them to their deterministic counterparts. pLSM offer more flexibility due to the probabilistic parameters resulting in a better performance for some values of these parameters.
- ItemBehavioral Outcomes and Neural Network Modeling of a Novel, Putative, Recategorization Sound Therapy(MDPI AG, 2021-04-27) Durai, M; Doborjeh, Z; Sanders, PJ; Vajsakovic, D; Wendt, A; Searchfield, GDThe mechanisms underlying sound’s effect on tinnitus perception are unclear. Tinnitus activity appears to conflict with perceptual expectations of “real” sound, resulting in it being a salient signal. Attention diverted towards tinnitus during the later stages of object processing potentially disrupts high-order auditory streaming, and its uncertain nature results in negative psychological responses. This study investigated the benefits and neurophysiological basis of passive perceptual training and informational counseling to recategorize phantom perception as a more real auditory object. Specifically, it examined underlying psychoacoustic correlates of tinnitus and the neural activities associated with tinnitus auditory streaming and how malleable these are to change with targeted intervention. Eighteen participants (8 females, 10 males, mean age = 61.6 years) completed the study. The study consisted of 2 parts: (1) An acute exposure over 30 min to a sound that matched the person’s tinnitus (Tinnitus Avatar) that was cross-faded to a selected nature sound (Cicadas, Fan, Water Sound/Rain, Birds, Water and Bird). (2) A chronic exposure for 3 months to the same “morphed” sound. A brain-inspired spiking neural network (SNN) architecture was used to model and compare differences between electroencephalography (EEG) patterns recorded prior to morphing sound presentation, during, after (3-month), and post-follow-up. Results showed that the tinnitus avatar generated was a good match to an individual’s tinnitus as rated on likeness scales and was not rated as unpleasant. The five environmental sounds selected for this study were also rated as being appropriate matches to individuals’ tinnitus and largely pleasant to listen to. There was a significant reduction in the Tinnitus Functional Index score and subscales of intrusiveness of the tinnitus signal and ability to concentrate with the tinnitus trial end compared to baseline. There was a significant decrease in how strong the tinnitus signal was rated as well as ratings of how easy it was to ignore the tinnitus signal on severity rating scales. Qualitative analysis found that the environmental sound interacted with the tinnitus in a positive way, but participants did not experience change in severity, however, characteristics of tinnitus, including pitch and uniformity of sound, were reported to change. The results indicate the feasibility of the computational SNN method and preliminary evidence that the sound exposure may change activation of neural tinnitus networks and greater bilateral hemispheric involvement as the sound morphs over time into natural environmental sound; particularly relating to attention and discriminatory judgments (dorsal attention network, precentral gyrus, ventral anterior network). This is the first study that attempts to recategorize tinnitus using passive auditory training to a sound that morphs from resembling the person’s tinnitus to a natural sound. These findings will be used to design future-controlled trials to elucidate whether the approach used differs in effect and mechanism from conventional Broadband Noise (BBN) sound therapy.
- ItemBioinformatics: a knowledge engineering approach(IEEE, 2004) Kasabov, NThe paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other knowledge representation procedures. Examples of the KE approach, and especially of one of the recently developed techniques - evolving connectionist systems (ECOS), to challenging problems in bioinformatics are given, that include: DNA sequence analysis, microarray gene expression profiling, protein structure prediction, finding gene regulatory networks, medical prognostic systems, computational neurogenetic modeling.
- ItemBrain-gene ontology: integrating bioinformatics and neuroinformatics data, information and knowledge to enable discoveries(IEEE, 2006) Kasabov, N; Jain, V.; Gottgtroy, P.; Benuskova, L.; Joseph, F.The paper presents some preliminary results on the brain-gene ontology (BGO) project that is concerned with the collection, presentation and use of knowledge in the form of ontology. BGO includes various concepts, facts, data, software simulators, graphs, videos, animations, and other information forms, related to brain functions, brain diseases, their genetic basis and the relationship between all of them. The first version of the brain-gene ontology has been completed as a hierarchical structure and as an initial implementation in the Protégé ontology building environment.
- ItemBrain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI(MDPI AG, 2023-11-21) Kasabov, Nikola K; Bahrami, Helena; Doborjeh, Maryam; Wang, AlanHumans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.
- ItemClassification and segmentation of fMRI Spatio-temporal Brain Data with a NeuCube Evolving Spiking Neural Network Model(IEEE, 2014) Doborjeh, M; Capecci, E; Kasabov, NThe proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured according to neurons spatial location inside the brain and their signals oscillating over the mental activity period ; thus, it is challenging to analyse and model dynamically. This paper addresses the problem by means of a novel Spiking Neural Networks (SNN) architecture, called NeuCube . After the NeuCube is trained with the fMRI samples, the `hidden' spatio- temporal relationship between data is learnt. Different cognitive states of the brain are activated while a subject is reading different sentences in terms of their polarity (affirmative and negative sentences). These are visualised via the SNN cube (SNNc) and then recognized through its classifier. The excellent classification accuracy of 90% proves the NeuCube potential in capturing the fMRI data information and classifying it correctly. The significant improvement in accuracy is demonstrated as compared with some already published results  on the same data sets and traditional machine learning methods. Future works is based on the proposed NeuCube model are also discussed in this paper.
- ItemComputational neurogenetic modeling: a methodology to study gene interactions underlying neural oscillations(IEEE, 2006) Benuskova, L.; Wysoski, S.; Kasabov, NWe present new results from Computational Neurogenetic Modeling to aid discoveries of complex gene interactions underlying oscillations in neural systems. Interactions of genes in neurons affect the dynamics of the whole neural network model through neuronal parameters, which change their values as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and neuronal parameters, particular target states of the neural network operation can be achieved, and statistics about gene interaction matrix can be extracted. In such a way it is possible to model the role of genes and their interactions in different brain states and conditions. Experiments with human EEG data are presented as an illustration of this methodology and also, as a source for the discovery of unknown interactions between genes in relation to their impact on brain activity. © 2006 IEEE.
- ItemComputational neurogenetic modelling: gene networks within neural networks(IEEE, 2004) Kasabov, N; Benuskova, L.; Gomes Wysoski, S.This paper introduces a novel connectionist approach to neural network modelling that integrates dynamic gene networks within neurons with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network. Through tuning the gene interaction network and the initial gene/protein expression values, different states of the neural network operation can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). Functioning of the SNN can be evaluated for instance by the field potentials, thus making it possible to attempt modelling the role of genes in different brain states such as epilepsy, schizophrenia, and other states, where EEG data is available to test the model predictions.
- ItemDENFIS: dynamic evolving neural-fuzzy inference system and its application for time series prediction(IEEE, 2002) Kasabov, N; Song, Q.This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.
- ItemDynamic 3D Clustering of Spatio-temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI and EEG Data(Springer, 2015-02-19) Gholami, M; Kasabov, NThe paper presents a novel clustering method for dynamic Spatio-Temporal Brain Data (STBD) on the case study of functional Magnetic Resonance Image (fMRI). The method is based on NeuCube spiking neural network (SNN) architecture, where the spatio-temporal relationships between STBD streams are learned and simultaneously the clusters are created. The clusters are represented as groups of spiking neurons inside the NeuCube’s spiking neural network cube (SNNc). The centroids of the clusters are predefined by spatial location of the brain data sources used as input variables. We illustrate the proposed clustering method on an fMRI case study STBD recorded during a cognitive task. A comparative analysis of the clusters across different mental activities can reveal new findings about the brain processes under study.
- ItemEffective Air Pollution Prediction by Combining Time Series Decomposition with Stacking and Bagging Ensembles of Evolving Spiking Neural Networks(Elsevier, 2023-10-28) Maciąg, Piotr S; Bembenik, Robert; Piekarzewicz, Aleksandra; Del Ser, Javier; Lobo, Jesus L; Kasabov, Nikola KIn this article, we introduce a new approach to air pollution prediction using the CEEMDAN time series decomposition method combined with the two-layered ensemble of predictors created based on the stacking and bagging techniques. The proposed ensemble approach is outperforming other selected state-of-the-art models when the bagging ensemble consisting of evolving Spiking Neural Networks (eSNNs) is used in the second layer of the stacking ensemble. In our experiments, we used the PM10 air pollution and weather dataset for Warsaw. As the results of the experiments show, the proposed ensemble can achieve the following error and agreement values over the tested dataset: error RMSE 6.91, MAE 5.14 and MAPE 21%; agreement IA 0.94. In addition, this article provides the computational and space complexity analysis of eSNNs predictors and offers a new encoding method for spiking neural networks that can be effectively applied for values of skewed distributions.
- ItemEfficient global clustering using the greedy elimination method(IEEE, 2004) Chan, Z.; Kasabov, NA novel global clustering method called the greedy elimination method is presented. Experiments show that the proposed method scores significantly lower clustering errors than the standard K-means over two benchmark and two application datasets, and it is efficient for handling large datasets.