SSPictR

Official repository for SSPictR: A Biologically-plausible Image Representation presented at the Artificial Intelligence and Cognition (AIC) workshop at ECAI 2025

SSPictR is a biologically plausible image representation based on spatial semantic pointers (SSPs). SSPictR encodes semantic labels of objects and their spatial locations extracted from segmentation maps. It only requires a single vector to capture a compressed but fully decodable neuro-symbolic representation of an image. We demonstrate the biological plausibility of SSPictR by performing representation similarity analysis, finding a significant correlation with fMRI data recorded from the early visual cortex. We further highlight the effectiveness and out-of-domain generalisability of SSPictR representations by training a compact model for scene recognition on standard benchmark datasets. Our simple neural network achieves performance on par with previous work, while having more than three times fewer trainable parameters. Taken together, SSPictR bridges the gap between biological plausibility and effective representations for tasks in computer vision and beyond.

Citation

Please cite this paper if you use SSPictR or parts of this code in your research:

@inproceedings{penzkofer25_aic,
  author = {Penzkofer, Anna and Habashy, Karim and Eliasmith, Chris and Bulling, Andreas},
  title = {SSPictR: A Biologically-plausible Image Representation},
  booktitle = {ECAI Workshop on Artificial Intelligence and Cognition (AIC)},
  year = {2025},
  pages = {1--13},
  doi = {}
}