Making a Spiking Net Work: Robust brain-like unsupervised machine learning
The surge in interest in Artificial Intelligence (AI) over the past decade has been driven almost exclusively by advances in Artificial Neural Networks (ANNs). While ANNs set state-of-the-art performance for many previously intractable problems, the use of global gradient descent necessitates large...
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Zusammenfassung: | The surge in interest in Artificial Intelligence (AI) over the past decade
has been driven almost exclusively by advances in Artificial Neural Networks
(ANNs). While ANNs set state-of-the-art performance for many previously
intractable problems, the use of global gradient descent necessitates large
datasets and computational resources for training, potentially limiting their
scalability for real-world domains. Spiking Neural Networks (SNNs) are an
alternative to ANNs that use more brain-like artificial neurons and can use
local unsupervised learning to rapidly discover sparse recognizable features in
the input data. SNNs, however, struggle with dynamical stability and have
failed to match the accuracy of ANNs. Here we show how an SNN can overcome many
of the shortcomings that have been identified in the literature, including
offering a principled solution to the dynamical "vanishing spike problem", to
outperform all existing shallow SNNs and equal the performance of an ANN. It
accomplishes this while using unsupervised learning with unlabeled data and
only 1/50th of the training epochs (labeled data is used only for a simple
linear readout layer). This result makes SNNs a viable new method for fast,
accurate, efficient, explainable, and re-deployable machine learning with
unlabeled data. |
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DOI: | 10.48550/arxiv.2208.01204 |