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Detection of bursts in neuronal spike trains by the mean inter-spike interval method

Progress in Natural Science:Materials international 2009年第2期19卷 229-235页

作者： Lin Chena,b,Yong Deng c,Weihua Luo a,Zhen Wang a,Shaoqun Zenga,a Hubei Bioinformatics and Molecular Imaging Key Laboratory,Huazhong University of Science and Technology,Wuhan 430074,China b College of Mechanical Engineering,Guangxi University,Nanning,530004,China c College of Optoelectronic Science of Engineering,Huazhong University of Science and Technology,Wuhan 430074,China Hubei Bioinformatics and Molecular Imaging Key Laboratory Huazhong University of Science and Technology Wuhan 430074 China College of Mechanical Engineering Guangxi University Nanning 530004 China College of Optoelectronic Science of Engineering Huazhong University of Science and Technology Wuhan 430074 China

Bursts are electrical spikes firing with a high frequency,which are the most important property in synaptic plasticity and information processing in the central nervous system. However,bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper,a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains,without any arbitrary choices or any operator judgment. When the mean value of several successive inter-spike intervals is not larger than the parameter,a burst is identified. By this method,bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays,as accurately as by visual inspection. Furthermore,significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

关键词： inter-spike interval Bursts spike trains Multi-electrode arrays

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Bio-inspired Tactile FA-I Spiking Generation under Sinusoidal Stimuli

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Journal of Bionic Engineering 2016年第4期13卷 612-621页

作者： Zhengkun Yi Yilei Zhang School of Mechanical and Aerospace Engineering Nanyang Technological University Manyang Avenue 639798 Singapore

For the design and development of advanced prosthetic limbs, many attempts have been made to restore the function of mechanoreceptors using artificial tactile sensors. Mechanoreceptors in human skin, which make dexterous manipulation pos- sible, respond to the mechanical stimuli in the form of spike trains. In this paper, a bin-inspired approach to replicate the Fast Adapting type I （FA-I） mechanoreceptor is developed, where piezoelectric materials, such as polyvinylidene difluoride （PVDF） films, are used to generate continuous analog electrical signals; then the analog signals are successfully converted into spike trains using the spiking neuron model. By comparing with spike trains measured from the glabrous skin of macaque monkeys, it was found that this approach can mimic FA-I afferent spiking activities in terms of both the average inter-spike interval and the first spike latency. spike features of the FA-I mechanoreceptors, such as the variability, frequency dependent responses, and population activity, were also explored, which may play a vital role in the understanding of the functionality of FA-I mech- anoreceptors and the development of advanced prosthetic limbs.

关键词： bio-inspired tactile sensor FA-I mechanoreceptor spiking neuron model inter-spike interval first spike latency

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