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This paper proposes replacing the standard point neuron model in artificial neural networks with a more realistic cortical cell model, claiming improvements in expressivity, robustness, learning speed, and reduced memorization and data requirements.
This paper investigates integrating dendritic neural networks with equilibrium propagation, showing that this biologically plausible approach improves performance on challenging datasets compared to standard equilibrium propagation.