A biological neuron's information processing is more complex than a perceptron
Biological neurons exhibit complex biophysical and nonlinear dendritic properties that enable more sophisticated information processing than standard perceptrons.
The retrieved literature consistently discusses the advanced computational capacities, nonlinear dendritic interactions, and biophysical complexities of biological neurons compared to traditional artificial neurons and perceptrons. Papers like [1], [3], and [5] emphasize that biological features such as dendritic plasticity and nonlinear integration allow single neurons to solve complex tasks that typically require multi-layer artificial structures. Therefore, the claim is supported.
L. Beaulieu-Laroche, Norma J. Brown, Marissa Hansen, Enrique H. S. Toloza, Jitendra Sharma, Ziv M. Williams, M. Frosch, G. Cosgrove, S. Cash, Mark T. Harnett. Allometric rules for mammalian cortical layer 5 neuron biophysics. 2021. https://doi.org/10.1038/s41586-021-04072-3
Highlights complex biophysical properties and morphology-dependent integrative mechanisms in biological neurons that go beyond simple point-neuron models.
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Andreas Stöckel, Chris Eliasmith. Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks. 2021. https://doi.org/10.1162/neco_a_01338
Demonstrates that nonlinear dendritic interactions provide computational resources far exceeding linear summation typically used in standard models.
Shi Wang, Daiki Sugiyama, Jian Sun, Lin Yang, Shangce Gao. Dendritic Neuron Model Trained by Biogeography-Based Optimization for Crude Oil Price Forecasting. 2018. https://doi.org/10.1109/IHMSC.2018.00017
Notes that single biological neurons with flexible dendritic plasticity perform complex computations and information processing superior to traditional multilayer perceptrons.
Chong Liu, Jingyan Ma, Songting Li, Douglas Zhou. Dendritic Integration Inspired Artificial Neural Networks Capture Data Correlation. 2024. https://doi.org/10.52202/079017-2519
Explores how incorporating biological dendritic integration rules into neural networks enhances computational capabilities and data representation.
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