Acupuncture and moxibustion act on acupoint areas with different frequencies, evoking a large number of responding activity of neurons to achieve the purpose of regulating human body functions. In the process of acupuncture, different frequencies of acupuncture evoked different neuronal spiking activity. In order to study the mechanism of acupuncture with different frequencies, Bayesian statistical model is used to optimize the results of the traditional classification algorithm based on spiking waveforms, which greatly reduces the missed detection rate of acupuncture responding activity. Then, the spiking events evoked by acupuncture at different frequencies were statistically analyzed, and the results showed that the number of neuronal spikes gradually increased with the increase of frequency. However, when the stimulation was increased to 120 times/min, the increase in the stimulation frequency will not evoke more spikes due to the saturation of frequency adaptation of the neurons. Finally, a probabilistic statistical model was used to encode the neuronal responding activity evoked by different acupuncture, and the maximum likelihood estimation method was used to fit the model parameters. The results show that the coupling parameters of stimulus are significantly smaller than the coupling parameters of spike-history, and the more the historical spikes, the smaller the coupling parameters of stimulus. This suggests that since acupuncture is a low-frequency mechanical stimulation, a large number of historical spikes in the spiking activity are the main factors that evoke the neuronal response. Thus, revealing the responding mechanism of different acupuncture frequencies.
Polyp segmentation has consistently been a difficult task because of the varying sizes of polyps and the significant intrinsic similarity between polyps and the surrounding tissues. To address the above problems, a contextual feature aggregation polyp segmentation algorithm combining Pyramid Vision Transformer and convolution (CFA-PVT) is proposed. Firstly, the Pyramid Vision Transformer is used to extract image global features, and the stage bridging module(SBM) is employed to enhance the ability of the network to handle polyp details and aggregate high-level polyp features. Subsequently, a feature enhancement module (FEM) is used to explore shallow polyp information. Finally, cross-layer feature fusion is performed by a global adaptive module (GAM) to realize feature interaction. This algorithm is evaluated on the CVC-ClinicDB and Kvasir-SEG datasets and further tested for generalization capability on the CVC-ColonDB dataset. The results demonstrate that the proposed method effectively segments colorectal polyp images, offering a new approach to diagnosing colorectal polyps.
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