The memristor crossbar architecture is a new technology that combines memory and computing on the same chip, finding numerous applications in modern bio-inspired computing systems. Recently, memristor-coupled Chua Circuit Networks (MCCNs) have been developed for the experimental confirmation of collective nonlinear phenomena, such as chimera states, that are also observed in the brain. For highly dense topologies, however, memristor crossbars can be prone to certain vulnerabilities. In this paper, we investigate the impact of sneak-path currents (SPCs) on the collective behaviors of chaotic oscillator networks, uncovering the network's tolerance to various realistic memristor crossbar designs. Despite the fact that these states alter the synchronization regime map, our findings suggest that SPCs have no detrimental impact on the formation and stability of single or multiple chimera states. This coupling issue along with other possible challenges are thoroughly discussed with a focus on nonlinear dynamics, highlighting the reliability of memristor crossbars as a coupling mechanism for studying chimera states.

Metastatic Melanoma (MM) is an aggressive type of cancer which produces metastases throughout the body with very poor survival rates. Recent advances in immunotherapy have shown promising results for controlling disease's progression. Due to the often rapid progression, fast and accurate diagnosis and treatment response assessment is vital for the whole patient management. These procedures prerequisite accurate, whole-body tumor identification. This can be offered by the imaging modality Positron Emission Tomography (PET)/Computed Tomography (CT) with the radiotracer F 18-Fluorodeoxyglucose (FDG). However, manual segmentation of PET/CT images is a very time-consuming and labor intensive procedure that requires expert knowledge. Most of the previously published segmentation techniques focus on a specific type of tumor or part of the body and require a great amount of manually labeled data, which is, however, difficult for MM. Multimodal analysis of PET/CT is also crucial because FDG-PET contains only the functional information of tumors which can be complemented by the anatomical information of CT. In this paper, we propose a whole-body segmentation framework capable of efficiently identifying the highly heterogeneous tumor lesions of MM from the whole-body 3D FDG-PET/CT images. The proposed decision support system begins with an Ensemble Unsupervised Segmentation of regions of high FDG-uptake based on Fuzzy C-means and a custom region growing algorithm. Then, a region classification model based on radiomics features and Neural Networks classifies these regions as tumors or not. Experimental results showed high performance in the identification of MM lesions with Sensitivity 83.68%, Specificity 91.82%, F1-score 75.42%, AUC 94.16% and Balanced accuracy 87.75% which were also supported by the public dataset evaluation.

Synchronization transition in neuromorphic networks has attracted much attention recently as a fundamental property of biological neural networks, which relies on network connectivity along with different synaptic features. In this work, an area-optimized FPGA implementation of an Asynchronous Cellular Automata Neuron model that exhibits discrete-state neuron dynamics is introduced. The proposed neuron model is capable of reproducing various neuromorphic oscillations observed in biological neurons using less hardware resources than previous implementations. We investigate synchronization transitions with a focus on the emergence of chimera states in a ring-based network consisting of hardware-based neurons with electrical synaptic coupling. In particular, we study the effects on the network’s phase synchronization through changing two control parameters: the coupling range and the coupling strength. We indicate that via proper configuration of the coupling parameters, we influence the synchronization transition and reveal chimera states which have been associated with neurological disorders.

The time series of the brain are usually characterized by the co-existence of synchronized and desynchronized behaviors. This kind of behavior is related to normal and disorderly functions of the brain. One of the suggested mechanisms to understand thoroughly this behavior are chimera states, which are characterized by the coincidence of coherent and incoherent dynamics that can be exploited through networks of symmetrically coupled identical oscillators. In this work, ring-based networks of Chua's circuits, the simplest electronic oscillators that perform chaotic and well-known bifurcation phenomena, have been extensively studied in memristive crossbars (Xbar), revealing various collective spatio-temporal behaviors, such as chimera states. With respect to different Xbar connectivities and via SPICE-level circuit simulations, the proposed Xbar system proves its efficacy to reproduce spatio-temporal patterns spanning from complete synchronization and chimera states up to fully chaotic states.