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    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Single dendritic neuron models (DNMs) are effective for nonlinear information processing in classification and prediction.
    • Complex-valued neural networks (CVNNs) have shown success in signal processing, but lack complex-valued single neuron architectures.

    Purpose of the Study:

    • To extend the dendritic neuron model (DNM) from real-valued to complex-valued domains.
    • To evaluate the performance of the proposed complex-valued DNM (CDNM).

    Main Methods:

    • Development of a complex-valued dendritic neuron model (CDNM).
    • Evaluation using complex XOR, non-minimum phase equalization, and wind prediction tasks.
    • Comparative analysis of activation functions and hyperparameter determination through experiments.

    Main Results:

    • The CDNM significantly outperforms real-valued DNMs.
    • The CDNM shows superior performance compared to complex-valued multi-layer perceptrons and other complex-valued neuron models.
    • Experimental validation across diverse complex-valued problems.

    Conclusions:

    • The proposed CDNM offers a novel and effective approach for complex-valued nonlinear information processing.
    • CDNM represents a significant advancement in single neuron architectures for complex-valued neural networks.
    • The CDNM shows broad applicability in various complex data processing tasks.