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12 April 2021 Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems
Yassin Khalifa, Justin Hawks, Ervin Sejdić
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Proceedings Volume 11730, Big Data III: Learning, Analytics, and Applications; 117300O (2021) https://doi.org/10.1117/12.2588610
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Recent advances in neuroscience have revealed many principles about neural processing. In particular, many biological systems were found to reconfigure/recruit single neurons to generate multiple kinds of decisions. Such findings have the potential to advance our understanding of the design and optimization process of artificial neural networks. Previous work demonstrated that dense neural networks are needed to shape complex decision surfaces required for AI-level recognition tasks. We investigate the ability to model high dimensional recognition problems using single or several neurons networks that are relatively easier to train. By employing three datasets, we test the use of a population of single neuron networks in performing multi-class recognition tasks. Surprisingly, we find that sparse networks can be as efficient as dense networks in both binary and multi-class tasks. Moreover, single neuron networks demonstrate superior performance in binary classification scheme and competing results when combined for multi-class recognition.
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Yassin Khalifa, Justin Hawks, and Ervin Sejdić "Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems", Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 117300O (12 April 2021); https://doi.org/10.1117/12.2588610
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KEYWORDS
Neural networks
Binary data
Neurons
Artificial neural networks
Neuroscience
Visualization
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