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藏经阁-Spiking Neural Networks, the N.pdf
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藏经阁-Spiking Neural Networks, the N.pdf
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DevinSoni
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cryptomarkets,datascience—100.github.io
Jan11 · 4minread
SpikingNeuralNetworks,theNext
GenerationofMachineLearning
Everyone who has been remotely tuned in to recent progress in
machine learning has heard of the current 2nd generation articial
neural networks used for machine learning. These are generally fully
connected, take in continuous values, and output continuous values.
Although they have allowed us to make breakthrough progress in many
elds, they are biologically inn-accurate and do not actually mimic the
actual mechanisms of our brain’s neurons.
The 3rd generation of neural networks, spiking neural networks, aims
to bridge the gap between neuroscience and machine learning, using
biologically-realistic models of neurons to carry out computation. A
spiking neural network (SNN) is fundamentally dierent from the
neural networks that the machine learning community knows. SNNs
operate using spikes, which are discrete events that take place at points
in time, rather than continuous values. The occurrence of a spike is
determined by dierential equations that represent various biological
processes, the most important of which is the membrane potential of
the neuron. Essentially, once a neuron reaches a certain potential, it
spikes, and the potential of that neuron is reset. The most common
model for this is the Leaky integrate-and-re (LIF) model. Additionally,
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