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The synchronization of coupled neurons has been an important field of study in neuroscience. In this paper, the synchronization in coupled map-based neurons is studied. It is assumed that the neurons are coupled via a memristor. Firstly, the case of two-coupled neurons is investigated, and then two neurons are used as the units of a ring network. It is shown that the memristive coupling coefficient and the initial condition of the flux variable affect the synchronization of two neurons. By increasing the memristive coupling coefficient, multiple synchronous and asynchronous regions are observed. In the ring network, two neurons in each unit can become synchronous, but the whole network does not reach complete synchronization.

Nowadays, automatic deception detection has received considerable attention in the machine learning community owing to this research interest to its vast applications in the fields of social media, interviews, law enforcement, and the military. In this study, a novel deep convolution neural network (DCNN) named LieNet is proposed to precisely detect the multi-scale variations of deception automatically. Our approach is a combination of contact and non-contact-based approaches. First, 20 frames from each video are fetched and concatenated to form a single image. Moreover, an audio signal is extracted from video and treated as image input by plotting the signal into two dimensional (2D) plane. Furthermore, 13 channels of EEG signals are plotted into 2D plane and concatenated to generate an image. Second, the LieNet model extracts features from each modality separately. Third, scores are estimated using a softmax classifier for all the modalities. Finally, three scores are combined using score level fusion to obtain a score, which gives support in favor of either deception or truth. The LieNet is validated on the “Bag-of-Lies (BoL)”“, Real-life (RL) trail”, and “Miami University Deception Detection (MU3D)" databases by considering four evaluation indexes viz., accuracy, precision, recall, and F1-score. Experimental outcomes depict that the LieNet defeats an initial work on Set-A and Set-B of the BoL database with average accuracies of 95.91% and 96.04% respectively. The accuracies obtained by the LieNet are 97% and 98% on RL trail and MU3D databases respectively.