Biodegradable and biocompatible graphene-based scaffolds for functional neural tissue engineering: A strategy approach using dental pulp stem cells and biomaterials.

Neural tissue engineering aims to restore the function of nervous system tissues using biocompatible cell-seeded scaffolds. Graphene-based scaffolds combined with stem cells deserve special attention to enhance tissue regeneration in a controlled manner. However, it is believed that minor changes in scaffold biomaterial composition, internal porous structure, and physicochemical properties can impact cellular growth and adhesion. The current work aims to investigate in vitro biological effects of three-dimensional (3D) graphene oxide (GO)/sodium alginate (GOSA) and reduced GOSA (RGOSA) scaffolds on dental pulp stem cells (DPSCs) in terms of cell viability and cytotoxicity. Herein, the effects of the 3D scaffolds, coating conditions, and serum supplementation on DPSCs functions are explored extensively. Biodegradation analysis revealed that the addition of GO enhanced the degradation rate of composite scaffolds. Compared to the 2D surface, the cell viability of 3D scaffolds was higher (p < 0.0001), highlighting the optimal initial cell adhesion to the scaffold surface and cell migration through pores. Moreover, the cytotoxicity study indicated that the incorporation of graphene supported higher DPSCs viability. It is also shown that when the mean pore size of the scaffold increases, DPSCs activity decreases. In terms of coating conditions, poly- l-lysine was the most robust coating reagent that improved cell-scaffold adherence and DPSCs metabolism activity. The cytotoxicity of GO-based scaffolds showed that DPSCs can be seeded in serum-free media without cytotoxic effects. This is critical for human translation as cellular transplants are typically serum-free. These findings suggest that proposed 3D GO-based scaffolds have favorable effects on the biological responses of DPSCs.

A pulse-frequency modulation sensor using memristive-based inhibitory interconnections.

This paper proposes a programmable inhibitory interconnection network between pixels in an array of novel low-voltage Schmitt-trigger-based PFM sensors that will be of interest for future applications in memristor-based early vision processing. In addition, a new low-power inverter-based pulse-frequency modulation (PFM) design and its integration with the network is also presented. To ensure no change in the memristors conductance in the network, the CMOS imager was designed for low voltage operation. That has resulted in a significant power reduction, better than 60%, and a comparable linear dynamic range when compared to published designs in the literature. The design was performed using a 0.13 um Samsung Electronics standard CMOS process, using 0.75 V supply voltage.

Integrated memristor-MOS (M2) sensor for basic pattern matching applications.

This paper introduces an integrated sensor circuit based on an analog Memristor-MOS (M2) pattern matching building block that calculates the similarity/dissimilarity between two analog values. A new approach for a pulse-width modulation pixel image sensor compatible with the memristive-MOS matching structure is introduced allowing direct comparison between incoming and stored images. The pulsed-width encoded information from the pixels is forwarded to a matching circuitry that provides an anti-Gaussian-like comparison between the states of memristors. The non-volatile and multi-state memory characteristics of memristor, together with the related ability to be programmed at any one of the intermediate states between logic '1' and logic '0' brings us closer to the implementation of bio-machines that can eventually emulate human-like sensory functions.

Advancing fabrication and properties of three-dimensional graphene-alginate scaffolds for application in neural tissue engineering.

Neural tissue engineering provides enormous potential for restoring and improving the function of diseased/damaged tissues and promising opportunities in regenerative medicine, stem cell technology, and drug discovery. The conventional 2D cell cultures have many limitations to provide informative and realistic neural interactions and network formation. Hence, there is a need to develop three-dimensional (3D) bioscaffolds to facilitate culturing cells with matched microenvironment for cell growth and interconnected pores for penetration and migration of cells. Herein, we report the synthesis and characterization of 3D composite bioscaffolds based on graphene-biopolymer with porous structure and improved performance for tissue engineering. A simple, eco-friendly synthetic method is introduced and optimized for synthesis of this hybrid fibrous scaffold by combining Graphene Oxide (GO) and Sodium Alginate (Na-ALG) which are specifically selected to match the mechanical strength of the central nervous system (CNS) tissue and provide porous structure for connective tissue engineering. Properties of the developed scaffold in terms of the structure, porosity, thermal stability, mechanical properties, and electrical conductivity are presented. These properties were optimised through key synthesis conditions including GO concentrations, reduction process and crosslinking time. In contrast to other studies, the presented structure maintains its stability in aqueous media and uses a bio-friendly reducing agent which enable the structure to enhance neuron cell interactions and act as nerve conduits for neurological diseases.

MOMSense: Metal-Oxide-Metal Elementary Glucose Sensor.

In this paper, we present a novel Pt/CuO/Pt metal-oxide-metal (MOM) glucose sensor. The devices are fabricated using a simple, low-cost standard photolithography process. The unique planar structure of the device provides a large electrochemically active surface area, which acts as a nonenzymatic reservoir for glucose oxidation. The sensor has a linear sensing range between 2.2 mM and 10 mM of glucose concentration, which covers the blood glucose levels for an adult human. The distinguishing property of this sensor is its ability to measure glucose at neutral pH conditions (i.e. pH = 7). Furthermore, the dilution step commonly needed for CuO-based nonenzymatic electrochemical sensors to achieve an alkaline medium, which is essential to perform redox reactions in the absence of glucose oxidase, is eliminated, resulting in a lower-cost and more compact device.

Memristive crypto primitive for building highly secure physical unclonable functions.

Physical unclonable functions (PUFs) exploit the intrinsic complexity and irreproducibility of physical systems to generate secret information. The advantage is that PUFs have the potential to provide fundamentally higher security than traditional cryptographic methods by preventing the cloning of devices and the extraction of secret keys. Most PUF designs focus on exploiting process variations in Complementary Metal Oxide Semiconductor (CMOS) technology. In recent years, progress in nanoelectronic devices such as memristors has demonstrated the prevalence of process variations in scaling electronics down to the nano region. In this paper, we exploit the extremely large information density available in nanocrossbar architectures and the significant resistance variations of memristors to develop an on-chip memristive device based strong PUF (mrSPUF). Our novel architecture demonstrates desirable characteristics of PUFs, including uniqueness, reliability, and large number of challenge-response pairs (CRPs) and desirable characteristics of strong PUFs. More significantly, in contrast to most existing PUFs, our PUF can act as a reconfigurable PUF (rPUF) without additional hardware and is of benefit to applications needing revocation or update of secure key information.

Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.

Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities.

A neuromorphic VLSI design for spike timing and rate based synaptic plasticity.

Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 µm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.