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- W4288085478 abstract "AbstractThis paper theorizes the concept of backpropagation in Spiking Neural Networks (SNN) using the reverse spiking mechanism. A Spiking Neural Network utilizes biologically-realistic models of neurons to operate, bridging the gap between neuroscience and machine learning. Leaky Integrate and Fire Neurons form the basis of this Spiking Neural Network, which is a combination of leaky resistors and capacitors. Synaptic currents I(t) serve as input to charge caps to produce potential V(t). Backpropagation in Spiking Neural Network has been a challenge for a long as its source of inspiration is biological neurons that naturally don’t backpropagate. In hardware implementation of deep learning algorithms, these are the most demanding metrics. Here we constructed a multilayered SNN following reverse-spike backpropagation and simulated it using Brian Simulator.KeywordsSpiking neural networkBackpropagationLeaky integrate and fire neuron modelReverse spiking" @default.
- W4288085478 created "2022-07-28" @default.
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- W4288085478 date "2022-01-01" @default.
- W4288085478 modified "2023-09-30" @default.
- W4288085478 title "Backpropagation in Spiking Neural Network Using Reverse Spiking Mechanism" @default.
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- W4288085478 doi "https://doi.org/10.1007/978-3-031-12413-6_40" @default.
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