The field of neural networks is moving towards more biologically inspired models, with a focus on improving object recognition and representation learning. Recent developments have shown that incorporating principles from biology, such as Hebbian learning and temporal continuity, can lead to more robust and transformation invariant features. Additionally, research has explored the use of predictive coding and tensor residual circuit neural networks to improve the generalization ability and robustness of neural networks. Noteworthy papers include:

• Improving VisNet for Object Recognition, which substantially improves recognition accuracy compared to the baseline model.
• A Tensor Residual Circuit Neural Network Factorized with Matrix Product Operation, which showcases more outstanding generalization and robustness with its average accuracies on various datasets 2%-3% higher than those of the state-of-the-art compared models.