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Irregular brain activity is of interest to researchers and scientists who are trying to understand, model, compare, and provide novel solutions to existing and challenging issues. Neurological disorders such as epilepsy, Alzheimer’s disease, Parkinson’s disease, and schizophrenia have been extensively studied. Among those diseases, epileptic seizures are the most commonly occurring ones. In this work, as a simplification of the complete biological operations of the brain, it was viewed as a system that consists of coupled oscillators. This allowed us to examine epilepsy as a pathological manifestation of the system. Emerging behaviors that arise from the spatiotemporal interactions of simple oscillators, namely, Chua’s Circuit, allowed us to observe how irregularities and changes to the coupling parameters of a neuromorphic network affect their synchronization and result in the emergence of epileptic activity. To achieve this, the characteristics of novel nanoelectronic devices, namely, memristors, have been exploited through their integration into two-dimensional crossbar arrays that offer the advantages of reprogrammability, low area, and low power consumption.

During the last years, a well studied biological substrate, namely Physarum polycephalum, has been proven efficient on finding appropriate and efficient solutions in hard to solve complex mathematical problems. The plasmodium of P. polycephalum is a single-cell that serves as a prosperous bio-computational example. Consequently, it has been successfully utilized in the past to solve a variety of path problems in graphs and combinatorial problems. In this work, this interesting behaviour is mimicked by a robust unconventional computational model, drawing inspiration from the notion of Cellular and Learning Automata. Namely, we employ principles of Cellular Automata (CAs) enriched with learning capabilities to develop a robust computational model, able of modelling appropriately the aforementioned biological substrate and, thus, capturing its computational capabilities. CAs are very efficient in modelling biological systems and solving scientific problems, owing to their ability of incarnating essential properties of a system where global behaviour arises as an effect of simple components, interacting locally. The resulting computational tool, after combining CAs with learning capabilities, should be appropriate for modelling the behaviour of living organisms. Thus, the inherent abilities and computational characteristics of the proposed bio-inspired model are stressed towards the experimental verification of Physarum's ability to model Logic Gates, while trying to find minimal paths in properly configured mazes with food sources. The presented simulation results for various Logic Gates are found in good agreement, both qualitatively and quantitatively, with the corresponding experimental results, proving the efficacy of this unconventional bio-inspired model and providing useful insights for its enhanced usage in various computing applications.

Lab-on-a-Chip technology promises many advantages compared to existing medical diagnostic tools including better and improved performance, portability, reliability and cost reduction. The Lab-on-a-Chip technology changed the traditional way by which biological samples are inspected in laboratories during analyses. A Lab-on-a-Chip is composed of four main parts; actuation, sensing, electronics and microfluidic packaging. Typically, hybrid technologies are used for the four parts, which represent difficulties in integration and increased cost. On the other hand, Complementary Metal Oxide Semiconductor (CMOS) technology allows the use of a single homogeneous technology to develop a fully integrated lab-on-a-chip. CMOS technology is a very well-established mass-production and cheap technology. Therefore, any viable Lab-on-a-Chip based on CMOS technology will have direct commercial value and application. This tutorial will cover the following aspects: • Quanttive, the four main parts of the Lab-on-a-chip (i.e. Actuation, Sensing, Read-out circuit and microfluidic packaging) • Concepts and State of the art in the area of CMOS Technology • Survey of the most common Lab-on-a-Chip techniques based on CMOS technology • Lab-on-a-chip architectures used for different biomedical applications, such as: DNA, PCR, biocells identification and characterization • Challenges and Future trends face the CMOS Based Lab-on-a-chip technology • The tutorial also will present the research results and microsystems that are developed and funded by Information Technology Industry Development Agency (ITIDA), Egypt, Project titled “A Microfluidic platform for liver cancer cells identification and characterizations”.