A Simulation Study of Self-organization in Neural Network by Spike-timing Dependent Plasticity

Abstract

Contemporary computers can operate very fast, so they can calculate more than about 109 times in a second. This computability is used in several fields like mathematics, science, engineering and so on mightily importantly. But, information that computers should process becomes complicated and heavy. Analyzing images, nature language process, and controlling 2 pair of walking robots are the examples. Contemporary computers cannot handle these problems. To solve this, a neuromorphic whose aim is generating machine whose operating principles are similar with an organics brain has been studied. Its goal is to generate a hardware which processes huge information by copying neuron and synapse that constituting the organics brain. Among them. The synapse has an important property called synaptic plasticity. It is known as one of important mechanisms forming memory and learning phenomenon. And nowadays, researches whose aim is to copy the synaptic plasticity using memristor have increased. Memristor is a material whose resistance is changeable depending on quantities of passing electric charge. It is paid attention as non-volatile memory device using this property. This paper is made up properties of spike-timing dependent plasticity(STDP) that is one of the synaptic plasticities and conditions of the memristor device for copying this phenomenon. A process of change of a neural network applied STDP using computer simulation is studied in this paper. Also, the memristor that is already reported is divided into 5 types depending on traits of resistance variation and differences generated when each memristor is applied are observed. At first, latency decrease effectiveness by STDP was studied. It was known that the STDP decreased latency through learning and made predicting an order of continuing input possible by existing researches. A computer simulation program was generated to study latency decrease phenomenon by STDP in a point of a nerve network. A neuron was simulated using Leaky Integrated-and-Fire(LIF) model, and the STDP is simulated using Triplet of spike model. Phenomenon of cooperation and competition by STDP were observed by a small-scaled nerve network using three neurons. It tended to happen the cooperation phenomenon when the less a time difference between signals was, and happen the competition phenomenon when the bigger a time difference between signals was, when more than two signals of neuron were input as a neuron. Paths were inclined to be converged to the shortest path by STDPs competition phenomenon in general-scaled nerve networks. This result of the convergence was similar with a result of Dijkstras algorithm that investigated the shortest paths. After learning by STDP, a time spending for propagation in a nerve network grew shorter than before learning according to potentiation of synapse corresponding to the shortest path. Next, research about circuits and materials for copying synapse using memristor is progressed. At first, issues in the integration, and material properties of the three-dimensional cross bar array synapses are dealt with in a quantitative manner. Two important quantitative guidelines for the memristive synapses integration are provided with respect to the required numbers of signal wires and sneak current paths. The merit of 3D cross bar arrays over 2D cross bar arrays (i.e., the decrease in effect memory cell size) can be realized only under certain limited conditions due to the increased area and layout complexity of the periphery circuits. The problem is quantitatively dealt with using the generalized equation for the overall resistance of the parasitic current paths. Next, The exist memristor was divided into 5 types : binary, linear, log, hard reset, hard set type. Then, STDP modified for each property of resistance variation was applied to them. A resistance value was controlled to show as a form of normal distribution using a normal distribution random number generator to replicate resistance variation phenomenon of actual device. Each result was compared with a result using ideal STDP model. As a result, in the case of the binary type, a degree of consistency decreased in proportion to variation value, and the variation value had less of an effect on the linear type. It declined greatly when the variation value was over 0.3 in the case of the log type. When it comes to the hard reset type, a degree of consistency was the highest among 5 types of memristor, and it was less influenced by variation. On the contrary, in the case of the hard set type, a degree of consistency was the lowest. Finally, atomic layer deposition was discussed in detail as the most feasible fabrication process of 3D CBA because it can provides the device with the necessary conformality and atomic-level accuracy in thickness control. In its final analysis, the STDP reinforced synapses that propagated signals most rapidly. It means that a nerve network is optimized to make propagation of signals fast by STDP. In addition, it was concluded that the hard reset type was most suitable for latency decrease effectiveness by STDP among other memristors that had diverse properties. It showed that when it comes to a change of weakening phenomenon of synapse, the bigger is the better, and when it comes to a change of strengthening phenomenon of synapse, the smaller is the better.