ReviewFrontiers in computational neuroscience2022
Symmetry-Based Representations for Artificial and Biological General Intelligence.
Review in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Principal bundle geometry of qualia: Understanding the quality of consciousness from symmetry.PNAS nexus · 2026Article
- Re-emergence of orientation coding in primate IT cortex and deep networks reveals functional hubs for visual processing.bioRxiv : the preprint server for biology · 2026Article
- The role of fibration symmetries in geometric deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Standardizing DICOM annotation: deep learning enhances body part description in X-ray image retrieval for clinical research.BMC medical imaging · 2025Article
- Investigating the Structure of Emotion: Tools, Pitfalls and Recommendations.Affective science · 2025Article
- Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation.PLoS computational biology · 2025Article
- Instance-level medical image classification for text-based retrieval in a medical data integration center.Computational and structural biotechnology journal · 2024Article
- Neural representational geometries reflect behavioral differences in monkeys and recurrent neural networks.Nature communications · 2024Article
- High-performing neural network models of visual cortex benefit from high latent dimensionality.PLoS computational biology · 2024Article
- Hierarchical VAEs provide a normative account of motion processing in the primate brain.bioRxiv : the preprint server for biology · 2023Article
- Symmetry and complexity in object-centric deep active inference models.Interface focus · 2023Article
- Abstract representations emerge naturally in neural networks trained to perform multiple tasks.Nature communications · 2023Article
- Compositional Sequence Generation in the Entorhinal-Hippocampal System.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological intelligence is remarkable in its ability to produce complex behavior in many diverse situations through data efficient, generalizable, and transferable skill acquisition. It is believed that learning "good" sensory representations is important for enabling this, however there is little agreement as to what a good representation should look like. In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation. The idea that there exist transformations (symmetries) that affect some aspects of the system but not others, and their relationship to conserved quantities has become central in modern physics, resulting in a more unified theoretical framework and even ability to predict the existence of new particles. Recently, symmetries have started to gain prominence in machine learning too, resulting in more data efficient and generalizable algorithms that can mimic some of the complex behaviors produced by biological intelligence. Finally, first demonstrations of the importance of symmetry transformations for representation learning in the brain are starting to arise in neuroscience. Taken together, the overwhelming positive effect that symmetries bring to these disciplines suggest that they may be an important general framework that determines the structure of the universe, constrains the nature of natural tasks and consequently shapes both biological and artificial intelligence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.