Spiking neural networks (SNNs) aim to replicate energy efficiency, learning speed and temporal processing of biological brains. However, accuracy and learning speed of such networks is still behind reinforcement learning (RL) models based on traditional neural models. This work combines a pre-trained binary convolutional neural network with an SNN trained online through reward-modulated STDP in order to leverage advantages of both models.
View Article and Find Full Text PDFThe mechanisms of KCl-induced enhancement in identification of individual molecules of poly(ethylene glycol) using solitary alpha-hemolysin nanoscale pores are described. The interaction of single molecules with the nanopore causes changes in the ionic current flowing through the pore. We show that the on-rate constant of the process is several hundred times larger and that the off-rate is several hundred times smaller in 4 M KCl than in 1 M KCl.
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