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The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
The summation of the motor units action potentials (MUAPs) in the rat medial gastrocnemius muscle was studied. Experiments were performed on anesthetized animals and single motor units (MUs) were functionally isolated by electrical stimulation of thin fi laments of ventral roots. MUAPs were recorded with two silver wires inserted into the muscle. Algebraic sum of the MUAPs recorded from two to four individual MUs were compared to the action potentials recorded during their simultaneous stimulation. The peak-to-peak amplitude, the longitudinal center of the signal square, the number of phases and turns, correlation coeffi cient and mean-square error were measured. In all studied cases of summation the number of phases and turns were the same in the two compared signals. When summation effects of two MUs were compared, there were no signifi cant differences in the peak-topeak amplitude. When 3 and 4 MUs were co-activated signifi cant differences between a sum of individual MUAPs and the recorded action potential were noted in some cases. The most variable parameter was the longitudinal center of the signal square which was the major source of differences. It was also shown that in a case of contractions of low force MUs the studied parameters of the algebraic sum of their MUAPs was similar to parameters of action potentials recorded during simultaneous stimulation.
The MMG signal generated in contracting pennate muscle is due to a transversal displacement of the its surface. It was shown on the base of in vivo experiments and a computer model that the MMG signal recorded during an isolated motor unit (MU) contraction is dependent on the stimulation frequency, the position of the laser distance sensor (LDS) and MU architecture. The three different profi les of the MMG can be observed. The fi rst type denoted as N (negative) is observed for the LDS located over the proximal connection of the MU (the distance from the muscle surface decreases with contraction force increase). In class P (positive) the relationship is opposite to N and the LDS is located over the area between distal connection of the MU and tendon of insertion. Finally, for the third class denoted as M (mixed) the MMG is initially positive, and when the contraction force exceed a certain level it starts to decrease and becomes negative. The process of the MMG summation during two MUs contraction was also investigated. It was observed that for the MUs with MMG-N or MMG-M profi le the MMG summation was quasi linear. In case of twitch and unfused contractions it was equal the algebraic sum of the individual MMGs. In case of the fused contraction the resulted MMG was slightly lesser than the algebraic sum. In opposition, the contraction of two MUs MMG-P induced the MMG signal lesser than the algebraic sum and the resulted signal was nearly equal the MMG presented by stronger MU.
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