Modeling of router structure for SNN-applicable NoC definitions

Spiking neural networks (SNN), as the third-generation artificial neural network, has a similar potential pulse triggering mechanism to the biological neuron. This mechanism enables the spiking neural network to increase computing power compared to the traditional artificial neural network to process complex information. However, a large number...

Population Step Forward Encoding Algorithm: Improving the signal encoding accuracy and efficiency of spike encoding algorithms

Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major problem that can occur in a specific use-case is the limited...

A robust modular spiking neural networks training methodology for time-series datasets: With a focus on gesture control

Neurons in Spiking Neural Networks (SNNs) communicate through spikes, similarly that neurons in the brain communicate, thus mimicking the brain. The working of SNNs is temporally based, as the spikes are time-dependent. SNNs have the benefit to perform continual classification, and are inherently more low-power than other Artificial Neural...