Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights.

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co40Fe40B20) х (LiNbO3-y )100-х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.

Effects of nanostructurized silicon on proliferation of stem and cancer cell.

In vitro experiments showed that stem and cancer cells retained their viability on the surface of porous silicon with 10-100 nm nanostructures, but their proliferation was inhibited. Silicon nanoparticles of 100 nm in size obtained by mechanical grinding of porous silicon films or crystal silicon plates in a concentration below 1 mg/ml in solution did not modify viability and proliferation of mouse fibroblast and human laryngeal cancer cells. Additional ultrasonic exposure of cancer cells in the presence of 1 mg/ml silicon nanoparticles added to nutrient medium led to complete destruction of cells or to the appearance of membrane defects blocking their proliferation and initiating their apoptotic death.

Plasmon induced modification of silicon nanocrystals photoluminescence in presence of gold nanostripes.

We report on the results of theoretical and experimental studies of photoluminescense of silicon nanocrystals in the proximity to plasmonic modes of different types. In the studied samples, the type of plasmonic mode is determined by the filling ratio of a one-dimensional array of gold stripes which covers the thin film with silicon nanocrystals on a quartz substrate. We analyze the extinction, photoluminesce spectra and decay kinetics of silicon nanocrystals and show that the incident and emitted light is coupled to the corresponding plasmonic mode. We demonstrate the modification of the extinction and photoluminesce spectra under the transition from wide to narrow gold stripes. The experimental extinction and photoluminescense spectra are in good agreement with theoretical calculations performed by the rigorous coupled wave analysis. We study the contribution of individual silicon nanocrystals to the overall photoluminescense intensity, depending on their spacial position inside the structure.

Green light activated hydrogen sensing of nanocrystalline composite ZnO-In2O3 films at room temperature.

The possibility of reducing the operating temperature of H2 gas sensor based on ZnO-In2O3 down to room temperature under green illumination is shown. It is found that sensitivity of ZnO-In2O3 composite to H2 nonmonotonically depends on the oxides' content. The optimal ratio between the components is chosen. The new mechanism of nanocrystalline ZnO-In2O3 sensor sensitivity to H2 under illumination by green light is proposed. The mechanism considers the illumination turns the composite into nonequilibrium state and the photoconductivity change in the H2 atmosphere is linked with alteration of nonequilibrium charge carriers recombination rate.