Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device.

Efforts to develop scalable learning algorithms for implementation of networks of spiking neurons in silicon have been hindered by the considerable footprints of learning circuits, which grow as the number of synapses increases. Recent developments in nanotechnologies provide an extremely compact device with low-power consumption.In particular, nanoscale resistive switching devices (resistive random-access memory (RRAM)) are regarded as a promising solution for implementation of biological synapses due to their nanoscale dimensions, capacity to store multiple bits and the low energy required to operate distinct states. In this paper, we report the fabrication, modeling and implementation of nanoscale RRAM with multi-level storage capability for an electronic synapse device. In addition, we first experimentally demonstrate the learning capabilities and predictable performance by a neuromorphic circuit composed of a nanoscale 1 kbit RRAM cross-point array of synapses and complementary metal-oxide-semiconductor neuron circuits. These developments open up possibilities for the development of ubiquitous ultra-dense, ultra-low-power cognitive computers.

Peripheral Pain Modulation of Chrysaora pacifica Jellyfish Venom Requires Both Ca2+ Influx and TRPA1 Channel Activation in Rats.

The venom of jellyfish triggers severe dermal pain along with inflammation and tissue necrosis, and occasionally, induces internal organ dysfunction. However, the basic mechanisms underlying its cytotoxic effects are still unknown. Here, we report one of the mechanisms involved in peripheral pain modulation associated with inflammatory and neurotoxic oxidative signaling in rats using the venom of jellyfish, Chrysaora pacifica (CpV). This jellyfish is identified by brown tentacles carrying nematocysts filled with cytotoxic venom that induces severe pain, pruritus, tentacle marks, and blisters. The subcutaneous injection of CpV into rat forepaws in behavioral tests triggered nociceptive response with a decreased threshold for mechanical pain perception. These responses lasted up to 48 h and were completely blocked by verapamil and TTA-P2, T-type Ca2+ channel blockers, or HC030031, a transient receptor potential cation ankyrin 1 (TRPA1) channel blocker, while another Ca2+ channel blocker, nimodipine, was ineffective. Also, treatment with Ca2+ chelators (EGTA and BaptaAM) significantly alleviated the CpV-induced pain response. These results indicate that CpV-induced pain modulation may require both Ca2+ influx through the T-type Ca2+ channels and activation of TRPA1 channels. Furthermore, CpV induced Ca2+-mediated oxidative neurotoxicity in the dorsal root ganglion (DRG) and cortical neurons dissociated from rats, resulting in decreased neuronal viability and increased intracellular levels of ROS. Taken together, CpV may activate Ca2+-mediated oxidative signaling to produce excessive ROS acting as an endogenous agonist of TRPA1 channels in the peripheral terminals of the primary afferent neurons, resulting in persistent inflammatory pain. These findings provide strong evidence supporting the therapeutic effectiveness of blocking oxidative signaling against pain and cytotoxicity induced by jellyfish venom.

Demonstration of Complementary Ternary Graphene Field-Effect Transistors.

Strong demand for power reduction in state-of-the-art semiconductor devices calls for novel devices and architectures. Since ternary logic architecture can perform the same function as binary logic architecture with a much lower device density and higher information density, a switch device suitable for the ternary logic has been pursued for several decades. However, a single device that satisfies all the requirements for ternary logic architecture has not been demonstrated. We demonstrated a ternary graphene field-effect transistor (TGFET), showing three discrete current states in one device. The ternary function was achieved by introducing a metal strip to the middle of graphene channel, which created an N-P-N or P-N-P doping pattern depending on the work function of the metal. In addition, a standard ternary inverter working at room temperature has been achieved by modulating the work function of the metal in a graphene channel. The feasibility of a ternary inverter indicates that a general ternary logic architecture can be realized using complementary TGFETs. This breakthrough will provide a key stepping-stone for an extreme-low-power computing technology.

Electronic system with memristive synapses for pattern recognition.

Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.