| name | cross-species-rsa-brain-alignment |
| description | Cross-Species RSA methodology for comparing brain-DNN alignment across human fMRI and macaque electrophysiology. Tests five learning rules (BP, FA, PC, STDP, untrained) across species showing conserved early visual alignment but divergent higher-area rankings. Use when: comparing species in brain encoding models, evaluating learning rule biological plausibility, cross-species validation of brain-DNN alignment, RSA with electrophysiology data. Triggered by: cross-species RSA, brain-DNN alignment macaque, learning rules comparison, V1 cross-species, macaque electrophysiology alignment, IT cross-species divergence, representational similarity analysis species. |
| category | ai_collection |
| tags | ["brain-DNN-alignment","cross-species","RSA","macaque-electrophysiology","visual-cortex","learning-rules","representational-similarity-analysis"] |
Cross-Species RSA: Conserved Early Visual Alignment but Divergent Higher-Area Rankings
arXiv: 2605.22401 (Submitted May 21, 2026)
Author: Nils Leutenegger
Categories: cs.LG, cs.NE, q-bio.NC
Core Question
Does the relationship between learning rules and brain alignment generalize across species?
This paper extends the finding that untrained CNNs match backpropagation at human V1 (arXiv:2604.16875) by testing the same five learning rules against macaque electrophysiology data.
Methods
Learning Rules Tested
- Backpropagation (BP) — standard supervised learning
- Feedback Alignment (FA) — random feedback weights
- Predictive Coding (PC) — local error propagation
- STDP — spike-timing-dependent plasticity
- Untrained random-weights baseline
Macaque Datasets
- MajajHong2015: V4/IT, 3,200 stimulus presentations, 88/168 neurons
- FreemanZiemba2013: V1/V2, 135 stimuli, 102/103 neurons
Analysis
- RSA (Representational Similarity Analysis) with identical model weights from the prior human fMRI study
- Multi-seed variability (5 seeds)
- Noise ceilings and stimulus-control analysis
Key Findings
1. Higher Absolute Alignment in Macaque
All models achieve higher alignment with macaque early visual cortex (ρ = 0.15–0.30 at V1/V2) than with human fMRI (ρ = 0.01–0.08), consistent with the higher signal-to-noise ratio of electrophysiology over fMRI BOLD.
2. STDP and PC Lead at V1/V2 (Cross-Species Conserved)
STDP produces the highest macaque V1/V2 alignment (ρ ~ 0.30), followed by PC (ρ ~ 0.28), consistent with their leading position among trained rules in human V1 — early visual alignment is robust across species.
3. IT Rankings Show No Cross-Species Correlation
At IT, learning rule rankings show no detectable correlation across species (Kendall's τ = 0.00, p = 1.00). However, this null result is limited by n = 5 (only detects τ = ±1.0) and is confounded by stimulus set differences between human and macaque experiments.
4. Model Capacity Dominates at IT
A pretrained ResNet-50 (ImageNet) achieves ρ = 0.25 at macaque IT, substantially above all custom CNN conditions (ρ = 0.07–0.14), suggesting IT alignment is limited by model capacity and training data rather than by the learning rule.
Implications
For Computational Neuroscience
- Early visual cortex (V1/V2) alignment with ANNs is conserved across primate species — validates human fMRI findings
- Higher visual areas (IT) show species-dependent rankings, driven by stimulus domain and model capacity rather than learning rule
- Electrophysiology provides 2–4x higher signal-to-noise for RSA compared to fMRI
For Brain-Model Alignment Research
- Cross-species validation is essential — human fMRI findings may not transfer to electrophysiological benchmarks
- IT alignment requires larger models and richer training data, not better learning rules
- Multi-species benchmarks needed for robust evaluation of neural coding hypotheses
Methodological Guidance
- Report noise ceilings and multi-seed variability in all RSA studies
- Control for stimulus differences when comparing across species
- Use identical model weights when comparing across datasets (this study does)
Related Skills
- untrained-cnns-match-backpropagation-v1-rsa (predecessor human fMRI study)
- naturality-violation-score (category-theoretic brain-DNN alignment)
- brain-dnn-transformation-alignment
Activation Keywords
- cross-species RSA
- brain-DNN alignment macaque
- learning rules comparison
- V1 cross-species conserved
- macaque electrophysiology alignment
- IT cross-species divergence
- representational similarity analysis species
- STDP macaque V1
- model capacity IT alignment
- pretrained ResNet vs learning rules
