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 dexter...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.展开更多
Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons...Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon as a slow leaky integrator, which captures almost all-known neural behaviors. The model controls the switching of axonal firing dynamics between passive conduction mode and persistent firing mode. The interplay between the axonal integrated potential and its multiple thresholds in axon precisely determines the persistent firing dynamics of neurons. We also present a persistent firing polychronous spiking network which exhibits asynchronous dynamics indicating that this computationally efficient model is not only bio-plausible, but also suitable for large scale spiking network simulations. The implications of this network and the analog circuit design for exploring the relationship between working memory and persistent firing enable developing a spiking network-based memory and bio-inspired computer systems.展开更多
文摘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.
文摘Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon as a slow leaky integrator, which captures almost all-known neural behaviors. The model controls the switching of axonal firing dynamics between passive conduction mode and persistent firing mode. The interplay between the axonal integrated potential and its multiple thresholds in axon precisely determines the persistent firing dynamics of neurons. We also present a persistent firing polychronous spiking network which exhibits asynchronous dynamics indicating that this computationally efficient model is not only bio-plausible, but also suitable for large scale spiking network simulations. The implications of this network and the analog circuit design for exploring the relationship between working memory and persistent firing enable developing a spiking network-based memory and bio-inspired computer systems.
