Neural networks : the official journal of the International Neural Network Society
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Discrete particle swarm optimization for identifying community structures in signed social networks.
Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. ⋯ In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising.
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We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods.
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This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the data are independent and identically distributed (i.i.d.). We evaluate the proposed method on four benchmark datasets and on the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC measurements collected over a period of 11 years. Our experimental results show that the prediction intervals produced by our method are both well calibrated and tight enough to be useful in practice.
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Disruption to different components of the prefrontal cortex, basal ganglia, and hippocampal circuits leads to various psychiatric and neurological disorders including Parkinson's disease (PD) and schizophrenia. Medications used to treat these disorders (such as levodopa, dopamine agonists, antipsychotics, among others) affect the prefrontal-striatal-hippocampal circuits in a complex fashion. We have built models of prefrontal-striatal and striatal-hippocampal interactions which simulate cognitive dysfunction in PD and schizophrenia. ⋯ In this paper, we review our past models and provide new simulation results for both PD and schizophrenia. We also describe an extended model that includes simulation of the different functional role of D1 and D2 dopamine receptors in the basal ganglia and prefrontal cortex, a dissociation we argue is essential for understanding the non-uniform effects of levodopa, dopamine agonists, and antipsychotics on cognition. Motivated by clinical and physiological data, we discuss model limitations and challenges to be addressed in future models of these brain disorders.
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In this paper, we first investigate the existence of a periodic solution to interval general bidirectional associative memory (BAM) neural networks with multiple delays on time scales by the continuation theorem of coincidence degree theory. Then, by constructing a Lyapunov functional, we discuss the global exponential stability of the periodic solution for such neural networks on time scales. The paper unifies periodic discrete-time and continuous-time BAM neural networks under the same framework.