摘要：SummaryClassical conditioning plays a critical role in the learning process of biological brains, and many computational models have been built to reproduce the related classical experiments. However, these models can reproduce and explain only a limited range of typical phenomena in classical conditioning. Based on existing biological findings concerning classical conditioning, we build a brain-inspired classical conditioning (BICC) model. Compared with other computational models, our BICC model can reproduce as many as 15 classical experiments, explaining a broader set of findings than other models have, and offers better computational explainability for both the experimental phenomena and the biological mechanisms of classical conditioning. Finally, we validate our theoretical model on a humanoid robot in three classical conditioning experiments (acquisition, extinction, and reacquisition) and a speed generalization experiment, and the results show that our model is computationally feasible as a foundation for brain-inspired robot classical conditioning.Graphical abstractDisplay OmittedHighlights•Classical conditioning (CC) is crucial in biological and embodied robot learning•A spiking neural network incorporates existing biological findings of CC in one model•BICC can explain a broader set of findings than other existing computational models•BICC ensures a robot gets similar biological behavior and speed generalization capabilityNeuroscience; cognitive neuroscience; artifical intelligence; robotics
