Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity

Spiking neural networks (SNNs) are believed to be highly computationally and energy efficient for specific neurochip hardware real-time solutions. However, there is a lack of learning algorithms for complex SNNs with recurrent connections, comparable in efficiency with back-propagation techniques and capable of unsupervised training. Here we suppose that each neuron in a biological neural network tends to maximize its activity in competition with other neurons, and put this principle at the basis of a new SNN learning algorithm. In such a way, a spiking network with the learned feed-forward, reciprocal and intralayer inhibitory connections, is introduced to the MNIST database digit recognition. It has been demonstrated that this SNN can be trained without a teacher, after a short supervised initialization of weights by the same algorithm. Also, it has been shown that neurons are grouped into families of hierarchical structures, corresponding to different digit classes and their associations. This property is expected to be useful to reduce the number of layers in deep neural networks and modeling the formation of various functional structures in a biological nervous system. Comparison of the learning properties of the suggested algorithm, with those of the Sparse Distributed Representation approach shows similarity in coding but also some advantages of the former. The basic principle of the proposed algorithm is believed to be practically applicable to the construction of much more complicated and diverse task solving SNNs. We refer to this new approach as “Family-Engaged Execution and Learning of Induced Neuron Groups”, or FEELING

Planar and 3D fibrous polyaniline-based materials for memristive elements

We discuss the effect of structure formation of Langmuir polyaniline layers on the performance of memristive thin-film elements as well as the morphology and conductivity of electrospinned PANI–PEO nonwovens

Modeling the Dynamics of Spiking Networks with Memristor-Based STDP to Solve Classification Tasks

The problem with training spiking neural networks (SNNs) is relevant due to the ultra-low power consumption these networks could exhibit when implemented in neuromorphic hardware. The ongoing progress in the fabrication of memristors, a prospective basis for analogue synapses, gives relevance to studying the possibility of SNN learning on the base of synaptic plasticity models, obtained by fitting the experimental measurements of the memristor conductance change. The dynamics of memristor conductances is (necessarily) nonlinear, because conductance changes depend on the spike timings, which neurons emit in an all-or-none fashion. The ability to solve classification tasks was previously shown for spiking network models based on the bio-inspired local learning mechanism of spike-timing-dependent plasticity (STDP), as well as with the plasticity that models the conductance change of nanocomposite (NC) memristors. Input data were presented to the network encoded into the intensities of Poisson input spike sequences. This work considers another approach for encoding input data into input spike sequences presented to the network: temporal encoding, in which an input vector is transformed into relative timing of individual input spikes. Since temporal encoding uses fewer input spikes, the processing of each input vector by the network can be faster and more energy-efficient. The aim of the current work is to show the applicability of temporal encoding to training spiking networks with three synaptic plasticity models: STDP, NC memristor approximation, and PPX memristor approximation. We assess the accuracy of the proposed approach on several benchmark classification tasks: Fisher’s Iris, Wisconsin breast cancer, and the pole balancing task (CartPole). The accuracies achieved by SNN with memristor plasticity and conventional STDP are comparable and are on par with classic machine learning approaches

The free and proprietary compilers C(C++) and Fortran at development of effective computing applications

The work presented by authors is devoted to features of implementation of algorithms of calculus mathematics with the aid of free and proprietary compilers. The analysis of speed of consecutive and parallel applications was carried out on the example of task of matrix multiplication, the solution of systems of the linear algebraic equations by exact and iterative methods. New technologies of parallel programming (auto parallelization, coarrays) are implemented only in modern proprietary compilers. Results of the analysis of high-speed performance of parallel programs of the high-precision calculations developed by authors are presented. Recommendations about the features of realization of task of calculus mathematics were worked out with the aid of various programming languages and compilers. Free and proprietary compilers allow to develop highly effective consecutive applications. Advantages of proprietary compilers are shown when developing parallel applications first of all due to support of the new technologies (auto parallelization, coarrays)

Pulsed Laser Phosphorus Doping and Nanocomposite Catalysts Deposition in Forming a-MoS<em>_x</em>/NP-Mo//n⁺p-Si Photocathodes for Efficient Solar Hydrogen Production

Pulsed laser deposition of nanostructured molybdenum sulfide films creates specific nonequilibrium growth conditions, which improve the electrocatalytic properties of the films in a hydrogen evolution reaction (HER). The enhanced catalytic performance of the amorphous a-MoSx (2 ≤ x ≤ 3) matrix is due to the synergistic effect of the Mo nanoparticles (Mo-NP) formed during the laser ablation of a MoS2 target. This work looks at the possibility of employing a-MoSx/NP-Mo films (4 and 20 nm thickness) to produce hydrogen by photo-stimulated HER using a p-Si cathode. A simple technique of pulsed laser p-Si doping with phosphorus was used to form an n+p-junction. Investigations of the energy band arrangement at the interface between a-MoSx/NP-Mo and n+-Si showed that the photo-HER on an a-MoSx/NP-Mo//n+p-Si photocathode with a 20 nm thick catalytic film proceeded according to a Z-scheme. The thickness of interfacial SiOy(P) nanolayer varied little in photo-HER without interfering with the effective electric current across the interface. The a-MoSx/NP-Mo//n+p-Si photocathode showed good long-term durability; its onset potential was 390 mV and photocurrent density was at 0 V was 28.7 mA/cm2. The a-MoSx/NP-Mo//n+p-Si photocathodes and their laser-based production technique offer a promising pathway toward sustainable solar hydrogen production