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Synaptic depression in deep neural networks for speech processing
2016
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to
doi:10.1109/icassp.2016.7472802
pmid:28286424
pmcid:PMC5344995
fatcat:pij3dyt6ufb4bdbry4htj7d2ze