… application of SNN. In this paper, we theoretically analyze ANN-SNN conversion and derive
sufficient conditions of the optimal conversion. To better correlate ANN-SNN and get greater …

… analyze ANN-SNN … -art ANN-SNN and directly trained SNNs in both accuracy and time-steps.
To the best of our knowledge, this is the first time to explore high-performance ANN-SNN …

… conversion by mapping the weights of a pre-trained ANN to neurons that can … neural
networks, we further demonstrated the neural function of synchronization on a neural network …

… of ANN to SNN can bring significant improvements. We introduce SNN Calibration, a cheap
… within a pre-trained Artificial Neural Network (ANN). Starting by analyzing the conversion …

… Neural Network clustering methods. Spiking Neural Network (SNN) models captured
neuronal behaviour more precisely than a traditional neural network as it contains the theory of …

… gradient problem for deep networks, making direct training … For these reasons, we focus
on ANN-to-SNN conversion … neural networks (BNNs) [1], making direct training and ANN-…

… neuron parameters (firing threshold and membrane leak). We propose DIET-SNN, a low-latency
deep spiking network … firing threshold along with other network parameters (weights). …

… we focus on Feed Forward Neural Network which is one of the simplest models of Artificial
Neural Network. The simplest kind of a FFNN is a Single layer Perceptron Network. It has only …

… during SNN inference. We propose ANN-SNN conversion using “soft reset” spiking neuron
model, referred to as Residual Membrane Potential (RMP) spiking neuron, which retains the …

… Based on the literature survey, artificial neural networks (ANN… The development of third
generation neural networks such … as the SNNSTLF (spiking neural network short-term load …