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Comparison of encoding schemes for spoken words in spiking neural networks
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2022-08-07
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en
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Abstract
Many recent studies have focused on speech recognition in the human brain
and an increasing part of this research uses spiking neural networks (SNNs)
for these investigations. SNNs, similar to the human brain, consist of neurons
that can communicate with each other by sending out spikes. The LKD
model is an example of such an SNN and an adapted version of this model
is used as a starting point for this research, which focuses specifically on the
way speech inputs are encoded in a spiking neural network and the human
brain. This encoding can be done in several ways and here three encoding
schemes are compared: the biologically plausible auditory encoding (BAE),
cochlear encoding and rate encoding. An accuracy-based comparison shows
that the BAE is the most optimal encoding for this SNN. These results
are unexpected because the Cochlear encoding is more biologically plausible
than the BAE and a sensible expectation would be that the most biologically
plausible encoding results in the highest accuracy.
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Faculteit der Sociale Wetenschappen