PUB - Publikationen an der Universität Bielefeld

Neural dendrites play a critical role in various cognitive functions, including spatial navigation, sensory processing, adaptive learning, and perception. The spatial layout, signal processing, and nonlinear dynamics of dendrites endow them with these abilities. However, designing an efficient learning mechanism with spiking dendrites remains a challenging problem. In this article, a novel biologically plausible learning method is developed to address this challenge. The method uses a multi-scale learning rule with dendritic predictive characteristics. A two-phase learning mechanism based on burst-related plateau potential dynamics of spiking dendrites is utilized to achieve global learning of the spiking model. Experimental results demonstrate that the proposed algorithm can improve the learning accuracy and reduce the synaptic operations compared to the previous dendritic learning rule without dendritic predictive mechanism. It effectively reduces both the synaptic operations and the spike number in the output layer, leading to a reduction of power consumption on neuromorphic hardware. This suggests the multi-scale combination of the three-factor dendritic prediction principle and two-phase plateau potential activities can enhance the learning capability and sparsity within a single neuron. Besides, our learning method enhances the robustness and improves the learning convergence speed. We also explore different model variation formations of our learning model. The proposed study can contribute to spike-based machine learning and neuromorphic computing. It is also meaningful for a deeper understanding of the dendritic roles on biologically plausible credit assignment in the brain cortex.