The Yamada model serves as the foundation for the computing model of the photonic SNN. We also provide an unique metric, the so-called spike sequence distance, to quantify the impact of controllable factors on the learning process. The numerical findings demonstrate that, using an iteration technique to continually update synaptic weights, the photonic SNN successfully replicates a desired output spike sequence in response to a spatiotemporal input spike pattern. These findings advance the co-design and optimization of the energy-efficient photonic SNN based entirely on VCSELs by one step.
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What is Spiking Neural Network?</h3>
Spiking neural networks, a third generation of neural networks, use physiologically accurate models of neurons to perform computation in an effort to close the gap between neuroscience and machine learning. The machine learning community is familiar with neural networks, but spiking neural networks (SNN) are fundamentally different. Instead than utilising continuous data, SNNs function on spikes, which are discrete events that happen at certain times in time. Differential equations that reflect multiple biological processes, the most significant of which is the membrane potential of the neuron, are used to predict when a spike will occur. In essence, when a neuron reaches a specific potential, it spikes, resetting the neuron's potential.
learn more about Spiking Neural Network refer:
brainly.com/question/28191615
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