Propaganda intelligence and covert action: the Regional Information Office and British intelligence in South-East Asia, 1949-1961

Shaw, Alexander Nicholas

doi:10.1080/16161262.2019.1659579

Cited by 3 publications

(1 citation statement)

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“…The neuron receives the pulse signal from the presynaptic unit to form the post-synaptic potential, and the potential difference with the fluid around the cell body forms the membrane voltage; if the membrane voltage accumulates to the threshold, the action potential will be triggered and transmitted to the post-synaptic neuron, Upon completion, the neuron enters a refractory period. In a certain period of time, no matter what input stimulus is received, no more action potential will be generated [14][15].…”

Section: The Spiking Neuron Modelmentioning

confidence: 99%

Geographical Image Feature Analysis of Spiking Neural Network Considering Regional Information

2020

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With the rapid development of remote sensing technology and computer technology, the quality of geographic image(GI) obtained by various remote sensing image acquisition methods is getting higher and higher, and the content of image expression is more and more complex. Degree requirements are getting higher and higher. In the GI obtained by remote sensing equipment, it can be seen that the global features of urban areas have a high degree of similarity. Most of the current image retrieval methods are based on global features, and a large number of repeated streets, residential areas, and commercial areas in urban GI make it difficult to It is difficult to retrieve regional information for geographic location positioning through traditional methods. Therefore, in this paper, a GI feature retrieval method based on spiking neural network(SNN) model, after comparing the effectiveness of SNN algorithm and LM-FNN, FNN, RNN and other neural network algorithms, it is verified that spiking neurons can optimize network performance. The experiments of GI texture feature extraction and local feature retrieval show that the proposed algorithm is more feasible in GI feature analysis.

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Section: The Spiking Neuron Modelmentioning

confidence: 99%