Search results for "Spike train"
showing 3 items of 13 documents
Hardware-accelerated spike train generation for neuromorphic image and video processing
2014
Recent studies concerning Spiking Neural Networks show that they are a powerful tool for multiple applications as pattern recognition, image tracking, and detection tasks. The basic functional properties of SNN reside in the use of spike information encoding as the neurons are specifically designed and trained using spike trains. We present a novel and efficient frequency encoding algorithm with Gabor-like receptive fields using probabilistic methods and targeted to FPGA for online pro-cessing. The proposed encoding is versatile, modular and, when applied to images, it is able to perform simple image transforms as edge detection, spot detection or removal, and Gabor-like filtering without a…
Changes in neural drive to calf muscles during steady submaximal contractions after repeated static stretches
2021
Key points: Repeated static-stretching interventions consistently increase the range of motion about a joint and decrease total joint stiffness, but findings on the changes in muscle and connective-tissue properties are mixed. The influence of these stretch-induced changes on muscle function at submaximal forces is unknown. To address this gap in knowledge, the changes in neural drive to the plantar flexor muscles after a static-stretch intervention were estimated. Neural drive to the plantar flexor muscles during a low-force contraction increased after repeated static stretches. These findings suggest that adjustments in motor unit activity are necessary at low forces to accommodate reduct…
Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics
2012
In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESCs), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing …