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Spike‐Timing‐Dependent Plasticity in Memristors

  • Shuai Y
  • Pan X
  • Sun X
InTech, (2018)
DOI: 10.5772/intechopen.69535
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Abstract

Abstract Long-haul travel does not constitute an obstacle for tourists to travel and is fast gaining the attention of tourists in new and unique experiences. This study was conducted to identify the long-haul travel motivation by international tourists to Penang. A total of 400 respondents participated in this survey, conducted around the tourist attractions in Penang, using cluster random sampling. However, only 370 questionnaires were only used for this research. Data were analysed using SPSS software 22 version. The findings, ‘knowledge and novelty seeking’ were the main push factors that drove long-haul travel by international tourists to Penang. Meanwhile, the main pull factor that attracts long- haul travel by international tourists to Penang was its ‘culture and history’. Additionally, there were partly direct and significant relationships between socio-demographic, trip characteristics and travel motivation (push factors and pull factors). Overall, this study identified the long-haul travel motivations by international tourists to Penang based on socio-demographic, trip characteristics and travel motivation and has indirectly helped in understanding the long-haul travel market particularly for Penang and Southeast Asia. This research also suggested for an effective marketing and promotion strategy in pro- viding useful information that is the key to attract international tourists to travel long distances. Keywords:

Figures

  • Figure 1. Memristor crossbar. (a) Integrated 12 × 12 crossbar with an Al 2 O 3 /TiO 2‐x memristor at each cross point. (b) I–V curve of the memristor. Inset (b): the cross‐sectional structure of the memristor device. (c) Absolute values of the change of memristor’s conductance under voltage pulses (with the width of 500 μs) of two polarities, as a function of the initial conductance, for various pulse amplitudes [10].
  • Figure 2. (a) The relationship between change of the memristor synaptic weight and the relative timing ∆t of the neuron spikes. The synaptic change was normalized to the maximum synaptic weight. Inset (a): SEM image of the crossbar structure of memristors. (b) The relationship between the change in excitatory postsynaptic current (EPSC) of rat hippocampal neurons after repetitive correlated spiking (60 pulses at 1 Hz) and relative spike timing. The figure was reconstructed with permission from Ref. [8, 12]. Inset (b) is the phase contrast image of a hippocampal neuron, which was adapted with permission from Ref. [4, 13, 26].
  • Figure 3. STDP synaptic characteristic of the memristor. Inset shows the anti‐STDP synaptic characteristic of the memristor [6].
  • Figure 4. Asymmetric STDP characteristic emulated in crossbar 4‐nm‐thick, 40 × 40 nm2 HfO 2 ‐based memristors [17].
  • Figure 5. Experimental results of the STDP characteristic of Pt/WO 3 /Pt memristor. (a) Current decay after the application of a sequence of positive and negative pulses was measured with reading voltage with the amplitude of 0.05 V. The transition from volatile to nonvolatile is indicated in the dotted square. (b) The relationship between the change of the synaptic weight and the relative timing of the prespike and postspike. Inset (b): waveform of prespike and postspike [3].
  • Figure 6. Nonlinear transmission characteristics and STDP of the memristor device. (a) Response of a memristor to different pulses; (b) emulation of STDP characteristics of memristor with the structure of Pt/HfO x /ZnO x /TiN—the relationship between the relative change of the memristor synaptic weight (ΔW) and the relative spike timing (Δt). And the solid line is the exponential fitting curve to the experimental data. The insets (b): schematics of various spikes.
  • Figure 7. Demonstration of STDP characteristics of memristor. (a) The variation of the current with the interval of voltage pulses. (b) The formation and decay of spike‐induced EPSC. (c and d) The preneuron spike and postneuron spike applied on the memristor for STDP. (e) The relationship between the relative change of the memristor synaptic weight (ΔW) and the relative spike timing (Δt). The exponential fitting results for the experimental data are illustrated by the solid lines in the graph.
  • Figure 8. Simulation of STDP. (a) EPSC. The preneuron spike was V+/V− = 2 V/−2 V. The current value gradually decayed back to zero within 50 ms after the spike. A pair of temporally correlated pulses with amplitudes V+/V− = 2 V/−2 V was applied to the TE and BE as preneuron spikes and postneuron spikes, respectively. (b) Δt is the interval between the beginning of the preneuron spikes and the beginning of the postneuron spikes. (c) STDP characteristics. The relationship between the change of synaptic weight and Δt defined in (b).

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APA

Shuai, Y., Pan, X., & Sun, X. (2018). Spike‐Timing‐Dependent Plasticity in Memristors. In Memristor and Memristive Neural Networks. InTech. https://doi.org/10.5772/intechopen.69535

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