Fig. 6: CH-HNN demonstrates adaptiveness and robustness in real-world applications.
From: Hybrid neural networks for continual learning inspired by corticohippocampal circuits
a The quadruped robot performs actions guided by MNIST code recognition using CH-HNN. b The robotic arm identifies and grasps a specific object (apple) based on CH-HNN's decision-making. c Average accuracy of various methods in real-world applications with different object positions and angles. The box plot displays the interquartile range (IQR) with Q3 (upper quartile) and Q1 (lower quartile), and outliers are shown as individual points. d Performance comparison under varying Gaussian noise (GN) levels for class-incremental learning on the sCIFAR-100 dataset. Results represent the distribution across five random seeds. e Power consumption analysis, comparing contributions from fully-connected layers (FC-L1 or L2), hybrid layers (Mask-L1 or L2), the output layer, and total consumption.
